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Sample records for fabricating convoluted shaped

  1. Patterned fabric defect detection via convolutional matching pursuit dual-dictionary

    NASA Astrophysics Data System (ADS)

    Jing, Junfeng; Fan, Xiaoting; Li, Pengfei

    2016-05-01

    Automatic patterned fabric defect detection is a promising technique for textile manufacturing due to its low cost and high efficiency. The applicability of most existing algorithms, however, is limited by their intensive computation. To overcome or alleviate the problem, this paper presents a convolutional matching pursuit (CMP) dual-dictionary algorithm for patterned fabric defect detection. A preprocessing with mean sampling is performed to eliminate the influence of background texture of fabric defects. Subsequently, a set of defect-free image blocks are selected as a sample set by sliding window. Dual-dictionary and sparse coefficiencies of the defect-free sample set are obtained via CMP and the K-singular value decomposition (K-SVD) based on a Gabor filter. Then we employ the defect-free and defective fabric image's projections onto the dual-dictionary as features for defect detection. Finally, the test results are determined by comparing the distance between the features to be measured. Experimental results reveal that the proposed algorithm is effective for patterned fabric defect detection and an acceptable average detection rate reaches by 94.2%.

  2. Illustrating Surface Shape in Volume Data via Principal Direction-Driven 3D Line Integral Convolution

    NASA Technical Reports Server (NTRS)

    Interrante, Victoria

    1997-01-01

    The three-dimensional shape and relative depth of a smoothly curving layered transparent surface may be communicated particularly effectively when the surface is artistically enhanced with sparsely distributed opaque detail. This paper describes how the set of principal directions and principal curvatures specified by local geometric operators can be understood to define a natural 'flow' over the surface of an object, and can be used to guide the placement of the lines of a stroke texture that seeks to represent 3D shape information in a perceptually intuitive way. The driving application for this work is the visualization of layered isovalue surfaces in volume data, where the particular identity of an individual surface is not generally known a priori and observers will typically wish to view a variety of different level surfaces from the same distribution, superimposed over underlying opaque structures. By advecting an evenly distributed set of tiny opaque particles, and the empty space between them, via 3D line integral convolution through the vector field defined by the principal directions and principal curvatures of the level surfaces passing through each gridpoint of a 3D volume, it is possible to generate a single scan-converted solid stroke texture that may intuitively represent the essential shape information of any level surface in the volume. To generate longer strokes over more highly curved areas, where the directional information is both most stable and most relevant, and to simultaneously downplay the visual impact of directional information in the flatter regions, one may dynamically redefine the length of the filter kernel according to the magnitude of the maximum principal curvature of the level surface at the point around which it is applied.

  3. Shape Engineered Nanoparticle Fabrication for Biomedical Applications

    NASA Astrophysics Data System (ADS)

    Nasrullah, Azeem

    Semiconductor fabrication research has developed technologies that allow for the deposition and patterning of thin films, and can be applied to many different industries, including the field of medicine. One such application is the fabrication of nanoparticles. There is a wide variety of nanoparticle-based medical diagnostics and therapies, including drug delivery and cancer imaging. Most of the nanoparticles being studied are chemically synthesized and spherical in shape, and studies have shown that other shapes can be more useful in certain applications, especially those that involve in vivo analysis and treatment. Fabrication of particles using a tool set developed from the semiconductor industry can allow for a detailed study of size and shape dependence on nanoparticle uptake in the bloodstream. Particle fabrication is achieved using thin film deposition, ion beam proximity lithography, wet etching, and lift-off, all similar to techniques commonly found in the semiconductor industry. The particles are formed using patterns developed with proximity lithography, and this represents the largest effort in this work. An ion beam, generated by a saddle-field ion source, is used to irradiate a polymeric resist with a thin membrane stencil mask placed in close proximity to the resist coated substrate in order to define the pattern. A saddle-field ion source was constructed and characterized for proximity lithography, with a beam diameter of 4.8 mm for a +/-5% tolerance in current density, a source size range of 0.3--0.9 mm, an average brightness value of 15 nAcm2˙sr , and average exposure times of ≈30 s. Stencil masks were fabricated from silicon nitride membranes in order to generate the pattern for the nanoparticles, and the particles were fabricated using a bi-layer resist and a sacrificial copper layer for release into solution.

  4. Styrene-based shape memory foam: fabrication and mathematical modeling

    NASA Astrophysics Data System (ADS)

    Yao, Yongtao; Zhou, Tianyang; Qin, Chao; Liu, Yanju; Leng, Jinsong

    2016-10-01

    Shape memory polymer foam is a promising kind of structure in the biomedical and aerospace field. Shape memory styrene foam with uniform and controlled open-cell structure was successfully fabricated using a salt particulate leaching method. Shape recovery capability exists for foam programming in both high-temperature compression and low-temperature compression (Shape recovery properties such as shape fixing property and shape recovery ratio were also characterized. In order to provide guidance for the future fabrication of shape memory foam, the theories of Gibson and Ashby as well as differential micromechanics theory were applied to predict Young’s modulus and the mechanical behavior of SMP styrene foams during the compression process.

  5. Variations in size, shape and asymmetries of the third frontal convolution in hominids: paleoneurological implications for hominin evolution and the origin of language.

    PubMed

    Balzeau, Antoine; Gilissen, Emmanuel; Holloway, Ralph L; Prima, Sylvain; Grimaud-Hervé, Dominique

    2014-11-01

    The study of brain structural asymmetries as anatomical substrates of functional asymmetries in extant humans, great apes, and fossil hominins is of major importance in understanding the structural basis of modern human cognition. We propose methods to quantify the variation in size, shape and bilateral asymmetries of the third frontal convolution (or posterior inferior frontal gyrus) among recent modern humans, bonobos and chimpanzees, and fossil hominins using actual and virtual endocasts. These methodological improvements are necessary to extend previous qualitative studies of these features. We demonstrate both an absolute and relative bilateral increase in the size of the third frontal convolution in width and length between Pan species, as well as in hominins. We also observed a global bilateral increase in the size of the third frontal convolution across all species during hominin evolution, but also non-allometric intra-group variations independent of brain size within the fossil samples. Finally, our results show that the commonly accepted leftward asymmetry of Broca's cap is biased by qualitative observation of individual specimens. The trend during hominin evolution seems to be a reduction in size on the left compared with the right side, and also a clearer definition of the area. The third frontal convolution considered as a whole projects more laterally and antero-posteriorly in the right hemisphere. As a result, the left 'Broca's cap' looks more globular and better defined. Our results also suggest that the pattern of brain asymmetries is similar between Pan paniscus and hominins, leaving the gradient of the degree of asymmetry as the only relevant structural parameter. As the anatomical substrate related to brain asymmetry has been present since the appearance of the hominin lineage, it is not possible to prove a direct relationship between the extent of variations in the size, shape, and asymmetries of the third frontal convolution and the origin of

  6. Fabrication of trough-shaped solar collectors

    DOEpatents

    Schertz, William W.

    1978-01-01

    There is provided a radiant energy concentration and collection device formed of a one-piece thin-walled plastic substrate including a plurality of nonimaging troughs with certain metallized surfaces of the substrate serving as reflective side walls for each trough. The one-piece plastic substrate is provided with a seating surface at the bottom of each trough which conforms to the shape of an energy receiver to be seated therein.

  7. Nanoparticles with tunable shape and composition fabricated by nanoimprint lithography

    NASA Astrophysics Data System (ADS)

    Alayo, Nerea; Conde-Rubio, Ana; Bausells, Joan; Borrisé, Xavier; Labarta, Amilcar; Batlle, Xavier; Pérez-Murano, Francesc

    2015-11-01

    Cone-like and empty cup-shaped nanoparticles of noble metals have been demonstrated to provide extraordinary optical properties for use as optical nanoanntenas or nanoresonators. However, their large-scale production is difficult via standard nanofabrication methods. We present a fabrication approach to achieve arrays of nanoparticles with tunable shape and composition by a combination of nanoimprint lithography, hard-mask definition and various forms of metal deposition. In particular, we have obtained arrays of empty cup-shaped Au nanoparticles showing an optical response with distinguishable features associated with the excitations of localized surface plasmons. Finally, this route avoids the most common drawbacks found in the fabrication of nanoparticles by conventional top-down methods, such as aspect ratio limitation, blurring, and low throughput, and it can be used to fabricate nanoparticles with heterogeneous composition.

  8. X-ray diffraction line profile analysis of nanostructured nickel oxide: Shape factor and convolution of crystallite size and microstrain contributions

    NASA Astrophysics Data System (ADS)

    Maniammal, K.; Madhu, G.; Biju, V.

    2017-01-01

    Nanostructured nickel oxide is synthesized through a chemical route and annealed at different temperatures. Contribution of crystallite size and microstrain to X-ray diffraction line broadening are analyzed by Williamson-Hall analysis using isotropic and anisotropic models. None of the models perform well in the case of samples with smaller average crystallite sizes. For sample with crystallite size 3 nm all models show negative slope which is physically meaningless. Analysis of shape factor shows that the line profiles are more Gaussian like. Size-strain plot method, which assumes a different convolution of the crystallite size and microstrain contributions, is found to be most suitable. The study highlights the fact that the convolution of crystallite size and microstrain contributions may differ for samples and should be taken into account while analyzing the observed line broadening. Microstrain values show a regular decrease with increase in the annealing temperature.

  9. Optofluidic fabrication for 3D-shaped particles

    PubMed Central

    Paulsen, Kevin S.; Di Carlo, Dino; Chung, Aram J.

    2015-01-01

    Complex three-dimensional (3D)-shaped particles could play unique roles in biotechnology, structural mechanics and self-assembly. Current methods of fabricating 3D-shaped particles such as 3D printing, injection moulding or photolithography are limited because of low-resolution, low-throughput or complicated/expensive procedures. Here, we present a novel method called optofluidic fabrication for the generation of complex 3D-shaped polymer particles based on two coupled processes: inertial flow shaping and ultraviolet (UV) light polymerization. Pillars within fluidic platforms are used to deterministically deform photosensitive precursor fluid streams. The channels are then illuminated with patterned UV light to polymerize the photosensitive fluid, creating particles with multi-scale 3D geometries. The fundamental advantages of optofluidic fabrication include high-resolution, multi-scalability, dynamic tunability, simple operation and great potential for bulk fabrication with full automation. Through different combinations of pillar configurations, flow rates and UV light patterns, an infinite set of 3D-shaped particles is available, and a variety are demonstrated. PMID:25904062

  10. Fabrication of a helical coil shape memory alloy actuator

    NASA Astrophysics Data System (ADS)

    Odonnell, R. E.

    1992-02-01

    A fabrication process was developed to form, heat treat, and join NiTi shape memory alloy helical coils for use as mechanical actuators. Tooling and procedures were developed to wind both extension and compression-type coils on a manual lathe. Heat treating fixtures and techniques were used to set the 'memory' of the NiTi alloy to the desired configuration. A swaging process was devised to fasten shape memory alloy extension coils to end fittings for use in actuator testing and for potential attachment to mechanical devices. The strength of this mechanical joint was evaluated.

  11. Fabrication of a helical coil shape memory alloy actuator

    SciTech Connect

    O'Donnell, R.E.

    1992-02-01

    A fabrication process was developed to form, heat treat, and join NiTi shape memory alloy helical coils for use as mechanical actuators. Tooling and procedures were developed to wind both extension and compression-type coils on a manual lathe. Heat treating fixtures and techniques were used to set the memory'' of the NiTi alloy to the desired configuration. A swaging process was devised to fasten shape memory alloy extension coils to end fittings for use in actuator testing and for potential attachment to mechanical devices. The strength of this mechanical joint was evaluated.

  12. Fabrication of a helical coil shape memory alloy actuator

    SciTech Connect

    O`Donnell, R.E.

    1992-02-01

    A fabrication process was developed to form, heat treat, and join NiTi shape memory alloy helical coils for use as mechanical actuators. Tooling and procedures were developed to wind both extension and compression-type coils on a manual lathe. Heat treating fixtures and techniques were used to set the ``memory`` of the NiTi alloy to the desired configuration. A swaging process was devised to fasten shape memory alloy extension coils to end fittings for use in actuator testing and for potential attachment to mechanical devices. The strength of this mechanical joint was evaluated.

  13. Compressed convolution

    NASA Astrophysics Data System (ADS)

    Elsner, Franz; Wandelt, Benjamin D.

    2014-01-01

    We introduce the concept of compressed convolution, a technique to convolve a given data set with a large number of non-orthogonal kernels. In typical applications our technique drastically reduces the effective number of computations. The new method is applicable to convolutions with symmetric and asymmetric kernels and can be easily controlled for an optimal trade-off between speed and accuracy. It is based on linear compression of the collection of kernels into a small number of coefficients in an optimal eigenbasis. The final result can then be decompressed in constant time for each desired convolved output. The method is fully general and suitable for a wide variety of problems. We give explicit examples in the context of simulation challenges for upcoming multi-kilo-detector cosmic microwave background (CMB) missions. For a CMB experiment with detectors with similar beam properties, we demonstrate that the algorithm can decrease the costs of beam convolution by two to three orders of magnitude with negligible loss of accuracy. Likewise, it has the potential to allow the reduction of disk space required to store signal simulations by a similar amount. Applications in other areas of astrophysics and beyond are optimal searches for a large number of templates in noisy data, e.g. from a parametrized family of gravitational wave templates; or calculating convolutions with highly overcomplete wavelet dictionaries, e.g. in methods designed to uncover sparse signal representations.

  14. EDITORIAL: Designer fabrication: nanotemplates get in shape Designer fabrication: nanotemplates get in shape

    NASA Astrophysics Data System (ADS)

    Demming, Anna

    2013-02-01

    People working in device design rarely see something that works without thinking how it could be made to work better. The work on anodic aluminum oxide materials in this issue provides a case in point [1]. Over the past century researchers have observed, manipulated and exploited the porous structures that result when anodizing aluminum in for example oxalic, sulfuric, and phosphoric acid solutions [1, 2]. The self-organized pore arrays have demonstrated the potential to facilitate high through-put, low-cost fabrication of nanocomposites as well as other nanostructures. The straight self-aligned nanochannels in porous anodic aluminum oxide (AAO) have long been accepted as an inherent property of these films and for many applications they are an attractive attribute. However, researchers in Taiwan have considered a novel manifestation of AAO materials which may enhance their natural attributes by generating arrays that bend [3]. Their work is an example of how even well studied systems continue to harbour surprises and scope for creative innovation. As the authors point out, 'This novel fan-out platform facilitates probing and handling many signals from different areas on a sample's surface and is therefore promising for applications in detection and manipulation at the nanoscale level'. It has long been recognized that the inter-pore distance, pore diameter and pore depth in AAO can be controlled by changing the anodization conditions. These accommodating features have motivated researchers to seek a better understanding of how to optimize fabrication conditions. A collaboration of researchers in Sweden, Chile and Uruguay studied the structural and optical properties of silver nanowires electrodeposited in commercially available nanoporous alumina templates, with a nominal pore diameter of 20 nm [4]. Their results revealed a decrease in the uniformity of pore filling with increasing deposition overpotential and suggested that overpotentials were preferred for the

  15. Fabrication and modeling of shape memory alloy springs

    NASA Astrophysics Data System (ADS)

    Heidari, B.; Kadkhodaei, M.; Barati, M.; Karimzadeh, F.

    2016-12-01

    In this paper, shape memory alloy (SMA) helical springs are produced by shape setting two sets of NiTi (Ti-55.87 at% Ni) wires, one of which showing shape memory effect and another one showing pseudoelasticity at the ambient temperature. Different pitches as well as annealing temperatures are tried to investigate the effect of such parameters on the thermomechanical characteristics of the fabricated springs. Phase transformation temperatures of the products are measured by differential scanning calorimetry and are compared with those of the original wires. Compression tests are also carried out, and stiffness of each spring is determined. The desired pitches are so that a group of springs experiences phase transition during loading while the other does not. The former shows a varying stiffness upon the application of compression, but the latter acts as passive springs with a predetermined stiffness. Based on the von-Mises effective stress and strain, an enhanced one-dimensional constitutive model is further proposed to describe the shear stress-strain response within the coils of an SMA spring. The theoretically predicted force-displacement responses of the produced springs are shown to be in a reasonable agreement with the experimental results. Finally, effects of variations in geometric parameters on the axial force-displacement response of an SMA spring are investigated.

  16. Thermocapillary Technique for Shaping and Fabricating Optical Ribbon Waveguides

    NASA Astrophysics Data System (ADS)

    Fiedler, Kevin; Troian, Sandra

    The demand for ever increasing bandwidth and higher speed communication has ushered the next generation optoelectronic integrated circuits which directly incorporate polymer optical waveguide devices. Polymer melts are very versatile materials which have been successfully cast into planar single- and multimode waveguides using techniques such as embossing, photolithography and direct laser writing. In this talk, we describe a novel thermocapillary patterning method for fabricating waveguides in which the free surface of an ultrathin molten polymer film is exposed to a spatially inhomogeneous temperature field via thermal conduction from a nearby cooled mask pattern held in close proximity. The ensuring surface temperature distribution is purposely designed to pool liquid selectively into ribbon shapes suitable for optical waveguiding, but with rounded and not rectangular cross sectional areas due to capillary forces. The solidified waveguide patterns which result from this non-contact one step procedure exhibit ultrasmooth interfaces suitable for demanding optoelectronic applications. To complement these studies, we have also conducted finite element simulations for quantifying the influence of non-rectangular cross-sectional shapes on mode propagation and losses. Kf gratefully acknowledges support from a NASA Space Technology Research Fellowship.

  17. Fabrication of Adhesive Lenses Using Free Surface Shaping

    NASA Astrophysics Data System (ADS)

    Hoheisel, D.; Kelb, C.; Wall, M.; Roth, B.; Rissing, L.

    2013-09-01

    Two approaches for fabricating polymer lenses are presented in this paper. Both are based on filling circular holes with UV curing adhesives. Initially, the viscous adhesive material creates a liquid and spherical free surface due to its own surface tension. This shape is then preserved by curing with UV-hardening light. For the first approach, the holes are generated in a 4 inch Si-wafer by deep reactive ion etching (DRIE) and for the second, a polydimethylsiloxane (PDMS) mould is manufactured. Three types of UV-curing adhesives are investigated (NOA 61, NOA 88 and NEA 121 by Norland Products). Preliminary to the determination of the lens curvature, a contact angle goniometer is used for taking side view images of the lenses. The radius of curvature is then extracted via image processing with the software MATLAB®. Furthermore, the surface roughness of the PDMS mould and the generated lenses is measured with a white light interferometer to characterize the casting process. The resolution power of the generated lenses is evaluated by measurement of their point spread functions (psf) and modulation transfer functions (mtf), respectively.

  18. Fabrication of shape memory nanofibers by electrospinning method

    NASA Astrophysics Data System (ADS)

    Zhang, Fenghua; Zhang, Zhichun; Liu, Yanju; Leng, Jinsong

    2013-04-01

    Shape memory nanofibers are capable of fixing a temporary shape and recovering a permanent shape in response to stimulus. Nafion nanofibers with shape memory effect are achieved via electrospinning technology. The resulting nanofibres exhibit the smooth, continuous, uniform fibrous structure. The diameter of nanofibers increases after annealing progress at different temperatures. The shape memory effect is evaluated in a fixed force controlled tensile test. Electrospun Nafion nanofibers show excellent shape memory properties upon heat. The shape fixity rates and shape recovery rates are about 95-96% and 87-89% after consecutive three shape memory cycles, respectively. The structure of electrospun nanofibers is stable and reversible for at least three cycles of shape memory tests. These results indicate that shape memory Nafion nanofibers can be used in a wide potential application fields such as smart materials and structures in the future.

  19. Fabrication of micro DOE using micro tools shaped with focused ion beam.

    PubMed

    Xu, Z W; Fang, F Z; Zhang, S J; Zhang, X D; Hu, X T; Fu, Y Q; Li, L

    2010-04-12

    A novel method is proposed to fabricate micro Diffractive Optical Elements (DOE) using micro cutting tools shaped with focused ion beam (FIB) milling. Micro tools with nanometric cutting edges and complicated shapes are fabricated by controlling the tool facet's orientation relative to the FIB. The tool edge radius of less than 30 nm is achieved for the nano removal of the work materials. Semi-circular micro tools and DOE-shaped micro tools are developed to fabricate micro-DOE and sinusoidal modulation templates. Experiments show that the proposed method can be a high efficient way in fabricating micro-DOE with nanoscale surface finishes.

  20. Method for fabricating uranium alloy articles without shape memory effects

    DOEpatents

    Banker, John G.

    1985-01-01

    Uranium-rich niobium and niobium-zirconium alloys possess a characteristic known as shape memory effect wherein shaped articles of these alloys recover their original shape when heated. The present invention circumvents this memory behavior by forming the alloys into the desired configuration at elevated temperatures with "cold" matched dies and maintaining the shaped articles between the dies until the articles cool to ambient temperature.

  1. Method for fabricating uranium alloy articles without shape memory effects

    DOEpatents

    Banker, J.G.

    1980-05-21

    Uranium-rich niobium and niobium-zirconium alloys possess a characteristic known as shape memory effect wherein shaped articles of these alloys recover their original shape when heated. The present invention circumvents this memory behavior by forming the alloys into the desired configuration at elevated temperatures with cold matched dies and maintaining the shaped articles between the dies until the articles cool to ambient temperature.

  2. Finite element modeling and fabrication of an SMA-SMP shape memory composite actuator

    NASA Astrophysics Data System (ADS)

    Souri, Mohammad

    Shape memory alloys and polymers have been extensively researched recently because of their unique ability to recover large deformations. Shape memory polymers (SMPs) are able to recover large deformations compared to shape memory alloys (SMAs), although SMAs have higher strength and are able to generate more stress during recovery. This project focuses on procedure for fabrication and Finite Element Modeling (FEM) of a shape memory composite actuator. First, SMP was characterized to reveal its mechanical properties. Specifically, glass transition temperature, the effects of temperature and strain rate on compressive response and recovery properties of shape memory polymer were studied. Then, shape memory properties of a NiTi wire, including transformation temperatures and stress generation, were investigated. SMC actuator was fabricated by using epoxy based SMP and NiTi SMA wire. Experimental tests confirmed the reversible behavior of fabricated shape memory composites. (Abstract shortened by ProQuest.).

  3. Adjustable reed for weaving net-shaped tailored fabrics

    NASA Technical Reports Server (NTRS)

    Farley, Gary L. (Inventor)

    1995-01-01

    An apparatus and method for forming woven fabrics through the use of an adjustable reed. The adjustable reed has multiple groups of reed wires that guide the warp yarns. The groups of reed wires move on reed rails parallel to the warp direction. In addition, rail expanders permit the space between the reed wires to be modified and telescoping rods attached to the rail sliders can be turned to permit the reed wires to be skewed to alter the fill yarn angle. These adjustments to the reed permit simultaneous variation of fill yarn angles and fabric widths and allow these variations to be made during fabrication, without the need to halt production.

  4. Fabrication of porous NiTi shape memory alloy structures using laser engineered net shaping.

    PubMed

    Krishna, B Vamsi; Bose, Susmita; Bandyopadhyay, Amit

    2009-05-01

    Porous NiTi alloy samples were fabricated with 12-36% porosity from equiatomic NiTi alloy powder using laser engineered net shaping (LENS). The effects of processing parameters on density and properties of laser-processed NiTi alloy samples were investigated. It was found that the density increased rapidly with increasing the specific energy input up to 50 J/mm(3). Further increase in the energy input had small effect on density. High cooling rates associated with LENS processing resulted in higher amount of cubic B2 phase, and increased the reverse transformation temperatures of porous NiTi samples due to thermally induced stresses and defects. Transformation temperatures were found to be independent of pore volume, though higher pore volume in the samples decreased the maximum recoverable strain from 6% to 4%. Porous NiTi alloy samples with 12-36% porosity exhibited low Young's modulus between 2 and 18 GPa as well as high compressive strength and recoverable strain. Because of high open pore volume between 36% and 62% of total volume fraction porosity, these porous NiTi alloy samples can potentially accelerate the healing process and improve biological fixation when implanted in vivo. Thus porous NiTi is a promising biomaterial for hard tissue replacements.

  5. Adjustable reed for weaving net-shaped tailored fabrics

    NASA Astrophysics Data System (ADS)

    Farley, Gary L.

    1994-06-01

    The invention is an apparatus and method for forming woven fabrics through the use of an adjustable reed. The adjustable reed has multiple groups of reed wires that guide the warp yarns. The groups of reed wires move on reed rails parallel to the warp direction. In addition, rail expanders permit the space between the reed wires to be modified and telescoping rods attached to the rail sliders can be turned to permit the reed wires to be skewed to alter the fill yarn angle. These adjustments to the reed permit simultaneous variation of fill yarn angles and fabric widths and allow these variations to be made during fabrication, without the need to halt production.

  6. Method for fabrication of cylindrical microlenses of selected shape

    DOEpatents

    Snyder, James J.; Baer, Thomas M.

    1992-01-01

    The present invention provides a diffraction limited, high numerical aperture (fast) cylindrical microlens. The method for making the microlens is adaptable to produce a cylindrical lens that has almost any shape on its optical surfaces. The cylindrical lens may have a shape, such as elliptical or hyperbolic, designed to transform some particular given input light distribution into some desired output light distribution. In the method, the desired shape is first formed in a glass preform. Then, the preform is heated to the minimum drawing temperature and a fiber is drawn from it. The cross-sectional shape of the fiber bears a direct relation to the shape of the preform from which it was drawn. During the drawing process, the surfaces become optically smooth due to fire polishing. The present invention has many applications, such as integrated optics, optical detectors and laser diodes. The lens, when connected to a laser diode bar, can provide a high intensity source of laser radiation for pumping a high average power solid state laser. In integrated optics, a lens can be used to couple light into and out of apertures such as waveguides. The lens can also be used to collect light, and focus it on a detector.

  7. Method for fabrication of cylindrical microlenses of selected shape

    DOEpatents

    Snyder, J.J.; Baer, T.M.

    1992-01-14

    The present invention provides a diffraction limited, high numerical aperture (fast) cylindrical microlens. The method for making the microlens is adaptable to produce a cylindrical lens that has almost any shape on its optical surfaces. The cylindrical lens may have a shape, such as elliptical or hyperbolic, designed to transform some particular given input light distribution into some desired output light distribution. In the method, the desired shape is first formed in a glass preform. Then, the preform is heated to the minimum drawing temperature and a fiber is drawn from it. The cross-sectional shape of the fiber bears a direct relation to the shape of the preform from which it was drawn. During the drawing process, the surfaces become optically smooth due to fire polishing. The present invention has many applications, such as integrated optics, optical detectors and laser diodes. The lens, when connected to a laser diode bar, can provide a high intensity source of laser radiation for pumping a high average power solid state laser. In integrated optics, a lens can be used to couple light into and out of apertures such as waveguides. The lens can also be used to collect light, and focus it on a detector. 11 figs.

  8. Progress in net shape fabrication of alpha sic turbine components

    NASA Technical Reports Server (NTRS)

    Sweeting, T. B.; Frechette, F. J.; Macbeth, J. W.

    1984-01-01

    An update of the status of ceramic component development of the AGT Programs is presented. Activity on AGTO Program focussed on the following: successful transition from the prototype to engine configuration rotor, investigation of alternate rotor molding techniques, and completion of scroll assemblies. Progress on the Garrett AGT Program was highlighted by the introduction of plastic molding and extrusion to parts which were previously fabricated by slip casting and isopressing respectively.

  9. Near net shape processing for solar thermal propulsion hardware using directed light fabrication

    SciTech Connect

    Milewski, J.O.; Fonseca, J.C.; Lewis, G.K.

    1998-12-01

    Directed light fabrication (DLF) is a direct metal deposition process that fuses gas delivered powder, in the focal zone of a high powered laser beam to form fully fused near net shaped components. The near net shape processing of rhenium, tungsten, iridium and other high temperature materials may offer significant cost savings compared with conventional processing. This paper describes a 3D parametric solid model, integrated with a manufacturing model, and creating a control field which runs on the DLF machine directly depositing a fully dense, solid metal, near net shaped, nozzle component. Examples of DLF deposited rhenium, iridium and tantalum, from previous work, show a continuously solidified microstructure in rod and tube shapes. Entrapped porosity indicates the required direction for continued process development. These combined results demonstrate the potential for a new method to fabricate complex near net shaped components using materials of interest to the space and aerospace industries.

  10. Scalable, Shape-specific, Top-down Fabrication Methods for the Synthesis of Engineered Colloidal Particles

    PubMed Central

    Merkel, Timothy J.; Herlihy, Kevin P.; Nunes, Janine; Orgel, Ryan M.; Rolland, Jason P.; DeSimone, Joseph M.

    2010-01-01

    The search for a method to fabricate non-spherical colloidal particles from a variety of materials is of growing interest. As the commercialization of nanotechnology continues to expand, the ability to translate particle fabrication methods from a laboratory to an industrial scale is of increasing significance. In this article, we examine several of the most readily scalable top-down methods for the fabrication of such shape specific particles and compare their capabilities with respect to particle composition, size, shape and complexity as well as the scalability of the method. We offer an extensive examination of Particle Replication In Non-wetting Templates (PRINT®) with regards to the versatility and scalability of this technique. We also detail the specific methods used in PRINT particle fabrication, including harvesting, purification and surface modification techniques, with examination of both past and current methods. PMID:20000620

  11. Understanding the microstructure and properties of components fabricated by laser engineered net shaping (LENS)

    SciTech Connect

    GRIFFITH,MICHELLE L.; ENSZ,MARK T.; PUSKAR,JOSEPH D.; ROBINO,CHARLES V.; BROOKS,JOHN A.; PHILLIBER,JOEL A.; SMUGERESKY,JOHN E.; HOFMEISTER,W.H.

    2000-05-18

    Laser Engineered Net Shaping (LENS) is a novel manufacturing process for fabricating metal parts directly from Computer Aided Design (CAD) solid models. The process is similar to rapid prototyping technologies in its approach to fabricate a solid component by layer additive methods. However, the LENS technology is unique in that fully dense metal components with material properties that are similar to that of wrought materials can be fabricated. The LENS process has the potential to dramatically reduce the time and cost required realizing functional metal parts. In addition, the process can fabricate complex internal features not possible using existing manufacturing processes. The real promise of the technology is the potential to manipulate the material fabrication and properties through precision deposition of the material, which includes thermal behavior control, layered or graded deposition of multi-materials, and process parameter selection. This paper describes the authors' research to understand solidification aspects, thermal behavior, and material properties for laser metal deposition technologies.

  12. Fabrication and Testing of a Leading-Edge-Shaped Heat Pipe

    NASA Technical Reports Server (NTRS)

    Glass, David E.; Merrigan, Michael A.; Sena, J. Tom; Reid, Robert S.

    1998-01-01

    The development of a refractory-composite/heat-pipe-cooled leading edge has evolved from the design stage to the fabrication and testing of a full size, leading-edge-shaped heat pipe. The heat pipe had a 'D-shaped' cross section and was fabricated from arc cast Mo-4lRe. An artery was included in the wick. Several issues were resolved with the fabrication of the sharp leading edge radius heat pipe. The heat pipe was tested in a vacuum chamber at Los Alamos National Laboratory using induction heating and was started up from the frozen state several times. However, design temperatures and heat fluxes were not obtained due to premature failure of the heat pipe resulting from electrical discharge between the induction heating apparatus and the heat pipe. Though a testing anomaly caused premature failure of the heat pipe, successful startup and operation of the heat pipe was demonstrated.

  13. Fabrication method for cores of structural sandwich materials including star shaped core cells

    DOEpatents

    Christensen, Richard M.

    1997-01-01

    A method for fabricating structural sandwich materials having a core pattern which utilizes star and non-star shaped cells. The sheets of material are bonded together or a single folded sheet is used, and bonded or welded at specific locations, into a flat configuration, and are then mechanically pulled or expanded normal to the plane of the sheets which expand to form the cells. This method can be utilized to fabricate other geometric cell arrangements than the star/non-star shaped cells. Four sheets of material (either a pair of bonded sheets or a single folded sheet) are bonded so as to define an area therebetween, which forms the star shaped cell when expanded.

  14. Fabrication method for cores of structural sandwich materials including star shaped core cells

    DOEpatents

    Christensen, R.M.

    1997-07-15

    A method for fabricating structural sandwich materials having a core pattern which utilizes star and non-star shaped cells is disclosed. The sheets of material are bonded together or a single folded sheet is used, and bonded or welded at specific locations, into a flat configuration, and are then mechanically pulled or expanded normal to the plane of the sheets which expand to form the cells. This method can be utilized to fabricate other geometric cell arrangements than the star/non-star shaped cells. Four sheets of material (either a pair of bonded sheets or a single folded sheet) are bonded so as to define an area therebetween, which forms the star shaped cell when expanded. 3 figs.

  15. Laser Spray Fabrication for Net-Shape Rapid Product Realization LDRD

    SciTech Connect

    Atwood, C.L.; Ensz, M.T.; Greene, D.L.; Griffith, M.L.; Harwell, L.D.; Jeantette, F.P.; Keicher, D.M.; Oliver, M.S.; Reckaway, D.E.; Romero, J.A.; Schlienger, M.E.; Smugeresky, J.D.

    1999-04-01

    The primary purpose of this LDRD project was to characterize the laser deposition process and determine the feasibility of fabricating complex near-net shapes directly from a CAD solid model. Process characterization provided direction in developing a system to fabricate complex shapes directly from a CAD solid model. Our goal for this LDRD was to develop a system that is robust and provides a significant advancement to existing technologies (e.g., polymeric-based rapid prototyping, laser welding). Development of the process will allow design engineers to produce functional models of their designs directly from CAD files. The turnaround time for complex geometrical shaped parts will be hours instead of days and days instead of months. With reduced turnaround time, more time can be spent on the product-design phase to ensure that the best component design is achieved. Maturation of this technology will revolutionize the way the world produces structural components.

  16. Multiplex lateral-flow test strips fabricated by two-dimensional shaping.

    PubMed

    Fenton, Erin M; Mascarenas, Monica R; López, Gabriel P; Sibbett, Scott S

    2009-01-01

    We have fabricated paper- and nitrocellulose-based lateral-flow devices that are shaped in two dimensions by a computer-controlled knife. The resulting star, candelabra, and other structures are spotted with multiple bioassay reagents to produce multiplex lateral-flow assays. We have also fabricated laminar composites in which porous nitrocellulose media are sandwiched between vinyl and polyester plastic films. This minimizes evaporation, protects assay surfaces from contamination and dehydration, and eliminates the need for the conventional hard plastic cassette holders that are typically used to package commercial lateral-flow diagnostic strips. The reported fabrication method is novel, low-cost, and well-suited to (i) fabrication and adoption in resource-poor areas, (ii) prototype development, (iii) high-volume manufacturing, and (iii) improving rates of operator error.

  17. Powder-Coated Towpreg: Avenues to Near Net Shape Fabrication of High Performance Composites

    NASA Technical Reports Server (NTRS)

    Johnston, N. J.; Cano, R. J.; Marchello, J. M.; Sandusky, D. A.

    1995-01-01

    Near net shape parts were fabricated from powder-coated preforms. Key issues including powder loss during weaving and tow/tow friction during braiding were addressed, respectively, by fusing the powder to the fiber prior to weaving and applying a water-based gel to the towpreg prior to braiding. A 4:1 debulking of a complex 3-D woven powder-coated preform was achieved in a single step utilizing expansion rubber molding. Also, a process was developed for using powder-coated towpreg to fabricate consolidated ribbon having good dimensional integrity and low voids. Such ribbon will be required for in situ fabrication of structural components via heated head advanced tow placement. To implement process control and ensure high quality ribbon, the ribbonizer heat transfer and pulling force were modeled from fundamental principles. Most of the new ribbons were fabricated from dry polyarylene ether and polymide powders.

  18. Laser Engineered Net Shaping (LENS(TM)): A Tool for Direct Fabrication of Metal Parts

    SciTech Connect

    Atwood, C.; Ensz, M.; Greene, D.; Griffith, M.; Harwell, L.; Reckaway, D.; Romero, T.; Schlienger, E.; Smugeresky, J.

    1998-11-05

    For many years, Sandia National Laboratories has been involved in the development and application of rapid prototyping and dmect fabrication technologies to build prototype parts and patterns for investment casting. Sandia is currently developing a process called Laser Engineered Net Shaping (LENS~) to fabricate filly dense metal parts dwectly from computer-aided design (CAD) solid models. The process is similar to traditional laser-initiated rapid prototyping technologies such as stereolithography and selective laser sintering in that layer additive techniques are used to fabricate physical parts directly from CAD data. By using the coordinated delivery of metal particles into a focused laser beam apart is generated. The laser beam creates a molten pool of metal on a substrate into which powder is injected. Concurrently, the substrate on which the deposition is occurring is moved under the beam/powder interaction zone to fabricate the desired cross-sectiwal geometry. Consecutive layers are additively deposited, thereby producing a three-dmensional part. This process exhibits enormous potential to revolutionize the way in which metal parts, such as complex prototypes, tooling, and small-lot production parts, are produced. The result is a comple~ filly dense, near-net-shape part. Parts have been fabricated from 316 stainless steel, nickel-based alloys, H13 tool steel, and titanium. This talk will provide a general overview of the LENS~ process, discuss potential applications, and display as-processed examples of parts.

  19. Fabrication and Characteristics of Free Standing Shaped Pupil Masks for TPF-Coronagraph

    NASA Technical Reports Server (NTRS)

    Balasubramanian, Kunjithapatham; Echternach, Pierre M.; Dickie, Matthew R.; Muller, Richard E.; White, Victor E.; Hoppe, Daniel J.; Shaklan, Stuart B.; Belikov, Ruslan; Kasdin, N. Jeremy; Vanderbei, Robert J.; Ceperley, Daniel; Neureuther, Andrew R.

    2006-01-01

    Direct imaging and characterization of exo-solar terrestrial planets require coronagraphic instruments capable of suppressing star light to 10-10. Pupil shaping masks have been proposed and designed1 at Princeton University to accomplish such a goal. Based on Princeton designs, free standing (without a substrate) silicon masks have been fabricated with lithographic and deep etching techniques. In this paper, we discuss the fabrication of such masks and present their physical and optical characteristics in relevance to their performance over the visible to near IR bandwidth.

  20. Diffractive optical elements fabricated for beam shaping of high-power diode lasers

    NASA Astrophysics Data System (ADS)

    Vogt, Helge; Biertümpfel, Ralf; Pawlowski, Edgar

    2008-02-01

    This paper discusses the use of diffractive optical elements (DOEs) and micro-optics fabricated by precise pressing in glass for beam shaping of high-power diode lasers. The DOEs are used to diffract the light into the point of interest and to improve the laser beam quality. We have realized circular, flat-top and multi-beam intensity profiles. The highest measured diffraction efficiency was higher than 95 %. The new established fabrication process has potential for mass production of DOEs. SCHOTT's precision glass molding process guarantees a very constant quality over the complete production chain.

  1. Fabrication of a smart air intake structure using shape memory alloy wire embedded composite

    NASA Astrophysics Data System (ADS)

    Jung, Beom-Seok; Kim, Min-Saeng; Kim, Ji-Soo; Kim, Yun-Mi; Lee, Woo-Yong; Ahn, Sung-Hoon

    2010-05-01

    Shape memory alloys (SMAs) have been actively studied in many fields utilizing their high energy density. Applying SMA wire-embedded composite to aerospace structures, such as air intake of jet engines and guided missiles, is attracting significant attention because it could generate a comparatively large actuating force. In this research, a scaled structure of SMA wire-embedded composite was fabricated for the air intake of aircraft. The structure was composed of several prestrained Nitinol (Ni-Ti) SMA wires embedded in ∩-shape glass fabric reinforced plastic (GFRP), and it was cured at room temperature for 72 h. The SMA wire-embedded GFRP could be actuated by applying electric current through the embedded SMA wires. The activation angle generated from the composite structure was large enough to make a smart air intake structure.

  2. Laundering durable antibacterial cotton fabrics grafted with pomegranate-shaped polymer wrapped in silver nanoparticle aggregations

    PubMed Central

    Liu, Hanzhou; Lv, Ming; Deng, Bo; Li, Jingye; Yu, Ming; Huang, Qing; Fan, Chunhai

    2014-01-01

    To improve the laundering durability of the silver functionalized antibacterial cotton fabrics, a radiation-induced coincident reduction and graft polymerization is reported herein where a pomegranate-shaped silver nanoparticle aggregations up to 500 nm can be formed due to the coordination forces between amino group and silver and the wrapping procedure originated from the coincident growth of the silver nanoparticles and polymer graft chains. This pomegranate-shaped silver NPAs functionalized cotton fabric exhibits outstanding antibacterial activities and also excellent laundering durability, where it can inactivate higher than 90% of both E. coli and S. aureus even after 50 accelerated laundering cycles, which is equivalent to 250 commercial or domestic laundering cycles. PMID:25082297

  3. Development of a method for fabricating metallic matrix composite shapes by a continuous mechanical process

    NASA Technical Reports Server (NTRS)

    Divecha, A. P.

    1974-01-01

    Attempts made to develop processes capable of producing metal composites in structural shapes and sizes suitable for space applications are described. The processes must be continuous and promise to lower fabrication costs. Special attention was given to the aluminum boride (Al/b) composite system. Results show that despite adequate temperature control, the consolidation characteristics did not improve as expected. Inadequate binder removal was identified as the cause responsible. An Al/c (aluminum-graphite) composite was also examined.

  4. Capacitive micromachined ultrasonic transducers with piston-shaped membranes: fabrication and experimental characterization.

    PubMed

    Huang, Yongli; Zhuang, Xuefeng; Haeggstrom, Edward O; Ergun, A Sanli; Cheng, Ching-Hsiang; Khuri-Yakub, Butrus T

    2009-01-01

    Capacitive micromachined ultrasonic transducers (CMUTs) featuring piston-shaped membranes (piston CMUTs) were developed to improve device performance in terms of transmission efficiency, reception sensitivity, and fractional bandwidth (FBW). A piston CMUT has a relatively flat active moving surface whose membrane motion is closer to ideal piston-type motion compared with a CMUT with uniformly thick membranes (classical CMUT). Piston CMUTs with a more uniform surface displacement profile can achieve high output pressure with a relatively small electrode separation. The improved device capacitance and gap uniformity also enhance detection sensitivity. By adding a center mass to the membrane, a large ratio of second-order resonant frequency to first-order resonant frequency was achieved. This improved the FBW. Piston CMUTs featuring membranes of different geometric shapes were designed and fabricated using wafer bonding. Fabricating piston CMUTs is a more complex process than fabricating CMUTs with uniformly thick membranes. However, no yield loss was observed. These devices achieved ~100% improvement in transduction performance (transmission and reception) over classical CMUTs. For CMUTs with square and rectangular membranes, the FBW increased from ~110% to ~150% and from ~140% to ~175%, respectively, compared with classical CMUTs. The new devices produced a maximum output pressure exceeding 1 MPa at the transducer surface. Performance optimization using geometric membrane shape configurations was the same in both piston CMUTs and classical CMUTs.

  5. Microfluidic Fabrication of Polymeric and Biohybrid Fibers with Predesigned Size and Shape

    PubMed Central

    Boyd, Darryl A.; Adams, Andre A.; Daniele, Michael A.; Ligler, Frances S.

    2014-01-01

    A “sheath” fluid passing through a microfluidic channel at low Reynolds number can be directed around another “core” stream and used to dictate the shape as well as the diameter of a core stream. Grooves in the top and bottom of a microfluidic channel were designed to direct the sheath fluid and shape the core fluid. By matching the viscosity and hydrophilicity of the sheath and core fluids, the interfacial effects are minimized and complex fluid shapes can be formed. Controlling the relative flow rates of the sheath and core fluids determines the cross-sectional area of the core fluid. Fibers have been produced with sizes ranging from 300 nm to ~1 mm, and fiber cross-sections can be round, flat, square, or complex as in the case with double anchor fibers. Polymerization of the core fluid downstream from the shaping region solidifies the fibers. Photoinitiated click chemistries are well suited for rapid polymerization of the core fluid by irradiation with ultraviolet light. Fibers with a wide variety of shapes have been produced from a list of polymers including liquid crystals, poly(methylmethacrylate), thiol-ene and thiol-yne resins, polyethylene glycol, and hydrogel derivatives. Minimal shear during the shaping process and mild polymerization conditions also makes the fabrication process well suited for encapsulation of cells and other biological components. PMID:24430733

  6. Fabrication and characterization of Pac-man shaped magnetic tunneling junctions

    NASA Astrophysics Data System (ADS)

    Han, Hongmei

    As the basic information cell in a magnetic random access memory (MRAM), magnetic tunneling junction (MTJ) and the addressing of its integration issues with the existing silicon technology are critical to improve its performance and competitiveness compared to other emerging RAM technologies. This dissertation uses micromagnetic modeling and microelectronic fabrication method to study the magnetic cell's shape effect on MTJ's magnetic and transport properties. In addition, integration of MTJ on the CMOS line processed metal pads is demonstrated with university facilities. From micromagnetic simulation, a proposed comma-shaped elongated Pac-man (EPM) shows the best-combined cell selectivity and switching characteristics compared to the existing ellipse and Saturn shapes. Its tilted effective easy axis broke the symmetry of the 'astroid', where enlarged operating window and increased half-selection resistance in the 1st and 3 rd quadrants are observed. A back-to-back paired configuration of two 180-EPMs (or half ellipse) and a multi-state MRAM paired cell design are proposed to effectively increase the MRAM density. The results from e-beam patterned magnetic elements match with the micromagnetic simulations. Various sized MTJs with rectangular, parallelogram, trapezoid and Pac-man shapes are fabricated and studied. Shape mainly affects MTJ's switching characteristics through the variation of its demagnetization field distribution. The integrated rectangular shaped MTJ tends to form vortex which reduces its MR signal up to 60%. MTJ with trapezoid shape shows better properties compared to the parallelogram shaped MTJ due to its confined demagnetic field configuration. Array of 8x8 180-EPM shaped MTJs have narrow switching field distribution. As MTJ's size reduces, its switching field increases noticeably. The quality of MTJ's barrier layer significantly affects its magnetoresistance (MR). Coupling between the free and fixed layers in a MTJ is detrimental to its MR

  7. Microstereolithography-Based Fabrication of Anatomically Shaped Beta-Tricalcium Phosphate Scaffolds for Bone Tissue Engineering

    PubMed Central

    Du, Dajiang; Asaoka, Teruo; Shinohara, Makoto; Kageyama, Tomonori; Ushida, Takashi; Furukawa, Katsuko Sakai

    2015-01-01

    Porous ceramic scaffolds with shapes matching the bone defects may result in more efficient grafting and healing than the ones with simple geometries. Using computer-assisted microstereolithography (MSTL), we have developed a novel gelcasting indirect MSTL technology and successfully fabricated two scaffolds according to CT images of rabbit femur. Negative resin molds with outer 3D dimensions conforming to the femur and an internal structure consisting of stacked meshes with uniform interconnecting struts, 0.5 mm in diameter, were fabricated by MSTL. The second mold type was designed for cortical bone formation. A ceramic slurry of beta-tricalcium phosphate (β-TCP) with room temperature vulcanization (RTV) silicone as binder was cast into the molds. After the RTV silicone was completely cured, the composite was sintered at 1500°C for 5 h. Both gross anatomical shape and the interpenetrating internal network were preserved after sintering. Even cortical structure could be introduced into the customized scaffolds, which resulted in enhanced strength. Biocompatibility was confirmed by vital staining of rabbit bone marrow mesenchymal stromal cells cultured on the customized scaffolds for 5 days. This fabrication method could be useful for constructing bone substitutes specifically designed according to local anatomical defects. PMID:26504839

  8. Net shape fabrication of calcium phosphate scaffolds with multiple material domains.

    PubMed

    Xie, Yangmin; Rustom, Laurence E; McDermott, Anna M; Boerckel, Joel D; Johnson, Amy J Wagoner; Alleyne, Andrew G; Hoelzle, David J

    2016-01-08

    Calcium phosphate (CaP) materials have been proven to be efficacious as bone scaffold materials, but are difficult to fabricate into complex architectures because of the high processing temperatures required. In contrast, polymeric materials are easily formed into scaffolds with near-net-shape forms of patient-specific defects and with domains of different materials; however, they have reduced load-bearing capacity compared to CaPs. To preserve the merits of CaP scaffolds and enable advanced scaffold manufacturing, this manuscript describes an additive manufacturing process that is coupled with a mold support for overhanging features; we demonstrate that this process enables the fabrication of CaP scaffolds that have both complex, near-net-shape contours and distinct domains with different microstructures. First, we use a set of canonical structures to study the manufacture of complex contours and distinct regions of different material domains within a mold. We then apply these capabilities to the fabrication of a scaffold that is designed for a 5 cm orbital socket defect. This scaffold has complex external contours, interconnected porosity on the order of 300 μm throughout, and two distinct domains of different material microstructures.

  9. Simulation and Fabrication of Wagon-Wheel-Shaped Piezoelectric Transducer for Raindrop Energy Harvesting Application

    NASA Astrophysics Data System (ADS)

    Wong, Chin Hong; Dahari, Zuraini; Jumali, Mohammad Hafizuddin; Mohamed, Khairudin; Mohamed, Julie Juliewatty

    2017-01-01

    Harvesting vibrational energy from impacting raindrops using piezoelectric material has been proven to be a promising approach for future outdoor applications, providing a good alternative resource that can be applied in outdoor rainy environments. We present herein an optimum novel polyvinylidene fluoride (PVDF) piezoelectric transducer specifically developed to harvest raindrop energy. The finite-element method was applied for simulation and optimization of the piezoelectric raindrop energy harvester (PREH) using COMSOL Multiphysics software, investigating the electrical potential, surface charge density, and total displacement for different transducer dimensions. According to the simulation results, the structure that generated the highest electrical potential and surface charge density was a wagon-wheel-shaped structure consisting of six spokes with wheel diameter of 30 mm, spoke width of 2 mm, center pad diameter of 6 mm, and thickness of 25 μm. This optimum wagon-wheel-shaped device was then fabricated by spin coating of PVDF, sputtering of aluminum, a poling process, and computer numerical control machining of a polytetrafluoroethylene stand. The fabricated PREH was characterized by x-ray diffraction analysis and Fourier-transform infrared spectroscopy. Finally, the fabricated PREH was tested under actual rain conditions with an alternating current to direct current converter connected in parallel, revealing that a single cell could generate average peak voltage of 22.5 mV and produce electrical energy of 3.4 nJ from ten impacts in 20 s.

  10. Simulation and Fabrication of Wagon-Wheel-Shaped Piezoelectric Transducer for Raindrop Energy Harvesting Application

    NASA Astrophysics Data System (ADS)

    Wong, Chin Hong; Dahari, Zuraini; Jumali, Mohammad Hafizuddin; Mohamed, Khairudin; Mohamed, Julie Juliewatty

    2017-03-01

    Harvesting vibrational energy from impacting raindrops using piezoelectric material has been proven to be a promising approach for future outdoor applications, providing a good alternative resource that can be applied in outdoor rainy environments. We present herein an optimum novel polyvinylidene fluoride (PVDF) piezoelectric transducer specifically developed to harvest raindrop energy. The finite-element method was applied for simulation and optimization of the piezoelectric raindrop energy harvester (PREH) using COMSOL Multiphysics software, investigating the electrical potential, surface charge density, and total displacement for different transducer dimensions. According to the simulation results, the structure that generated the highest electrical potential and surface charge density was a wagon-wheel-shaped structure consisting of six spokes with wheel diameter of 30 mm, spoke width of 2 mm, center pad diameter of 6 mm, and thickness of 25 μm. This optimum wagon-wheel-shaped device was then fabricated by spin coating of PVDF, sputtering of aluminum, a poling process, and computer numerical control machining of a polytetrafluoroethylene stand. The fabricated PREH was characterized by x-ray diffraction analysis and Fourier-transform infrared spectroscopy. Finally, the fabricated PREH was tested under actual rain conditions with an alternating current to direct current converter connected in parallel, revealing that a single cell could generate average peak voltage of 22.5 mV and produce electrical energy of 3.4 nJ from ten impacts in 20 s.

  11. Fabrication

    NASA Technical Reports Server (NTRS)

    Angel, Roger; Helms, Richard; Bilbro, Jim; Brown, Norman; Eng, Sverre; Hinman, Steve; Hull-Allen, Greg; Jacobs, Stephen; Keim, Robert; Ulmer, Melville

    1992-01-01

    What aspects of optical fabrication technology need to be developed so as to facilitate existing planned missions, or enable new ones? Throughout the submillimeter to UV wavelengths, the common goal is to push technology to the limits to make the largest possible apertures that are diffraction limited. At any one wavelength, the accuracy of the surface must be better than lambda/30 (rms error). The wavelength range is huge, covering four orders of magnitude from 1 mm to 100 nm. At the longer wavelengths, diffraction limited surfaces can be shaped with relatively crude techniques. The challenge in their fabrication is to make as large as possible a reflector, given the weight and volume constraints of the launch vehicle. The limited cargo diameter of the shuttle has led in the past to emphasis on deployable or erectable concepts such as the Large Deployable Reflector (LDR), which was studied by NASA for a submillimeter astrophysics mission. Replication techniques that can be used to produce light, low-cost reflecting panels are of great interest for this class of mission. At shorter wavelengths, in the optical and ultraviolet, optical fabrication will tax to the limit the most refined polishing methods. Methods of mechanical and thermal stabilization of the substrate will be severely stressed. In the thermal infrared, the need for large aperture is tempered by the even stronger need to control the telescope's thermal emission by cooled or cryogenic operation. Thus, the SIRTF mirror at 1 meter is not large and does not require unusually high accuracy, but the fabrication process must produce a mirror that is the right shape at a temperature of 4 K. Future large cooled mirrors will present more severe problems, especially if they must also be accurate enough to work at optical wavelengths. At the very shortest wavelengths accessible to reflecting optics, in the x-ray domain, the very low count fluxes of high energy photons place a premium on the collecting area. It is

  12. Fabrication

    NASA Astrophysics Data System (ADS)

    Angel, Roger; Helms, Richard; Bilbro, Jim; Brown, Norman; Eng, Sverre; Hinman, Steve; Hull-Allen, Greg; Jacobs, Stephen; Keim, Robert; Ulmer, Melville

    1992-08-01

    What aspects of optical fabrication technology need to be developed so as to facilitate existing planned missions, or enable new ones? Throughout the submillimeter to UV wavelengths, the common goal is to push technology to the limits to make the largest possible apertures that are diffraction limited. At any one wavelength, the accuracy of the surface must be better than lambda/30 (rms error). The wavelength range is huge, covering four orders of magnitude from 1 mm to 100 nm. At the longer wavelengths, diffraction limited surfaces can be shaped with relatively crude techniques. The challenge in their fabrication is to make as large as possible a reflector, given the weight and volume constraints of the launch vehicle. The limited cargo diameter of the shuttle has led in the past to emphasis on deployable or erectable concepts such as the Large Deployable Reflector (LDR), which was studied by NASA for a submillimeter astrophysics mission. Replication techniques that can be used to produce light, low-cost reflecting panels are of great interest for this class of mission. At shorter wavelengths, in the optical and ultraviolet, optical fabrication will tax to the limit the most refined polishing methods. Methods of mechanical and thermal stabilization of the substrate will be severely stressed. In the thermal infrared, the need for large aperture is tempered by the even stronger need to control the telescope's thermal emission by cooled or cryogenic operation. Thus, the SIRTF mirror at 1 meter is not large and does not require unusually high accuracy, but the fabrication process must produce a mirror that is the right shape at a temperature of 4 K. Future large cooled mirrors will present more severe problems, especially if they must also be accurate enough to work at optical wavelengths. At the very shortest wavelengths accessible to reflecting optics, in the x-ray domain, the very low count fluxes of high energy photons place a premium on the collecting area. It is

  13. Novel processes for near net-shaped fabrication of monolithic and reinforced oxide ceramics

    NASA Astrophysics Data System (ADS)

    Kumar, Pragati

    Mg reinforced Alsb2Osb3 composites were fabricated by pressureless infiltration of molten Mg into porous Alsb2Osb3 preforms. Such pressureless infiltration is thought to be driven by a displacement reaction that was observed to occur at interfaces between liquid Mg and solid Alsb2Osb3. The feasibility of fabricating near net-shaped, monolithic, MgAlsb2Osb4 spinel bodies by the oxidation of the solid Mg-Alsb2Osb3-bearing composites was demonstrated. By controlling the preform porosity and the infiltration conditions, Mg-Alsb2Osb3-bearing composite bodies could be produced with the proper overall stoichiometry for spinel. The Mg/Alsb2Osb3 composites could be machined into complex shapes. Oxidation of the Mg in the shaped composite was conducted in pure, flowing oxygen at 430{-}700sp°C. Post-oxidation annealing at 1200sp°C then allowed for complete conversion of MgO-Alsb2Osb3 bearing body into MgAlsb2Osb4 spinel. A final sintering treatment in flowing Ar at 1700sp°C yielded spinel specimens with densities ≥92%. The sintered spinel bodies retained the Mg-Alsb2Osb3-bearing precursor shape and dimensions (to within 0.63%). The fabrication of spinel-matrix composites is also discussed. In addition, a novel approach is presented for the fabrication of dense, shaped ceramic/metal composites by a novel class of displacement reactions. This approach differs from other oxidation-based processes for fabricating near net-shaped oxide/metal composites (e.g. DIMOX, Csp4) in that a reaction-induced volume expansion is used to compensate for the porosity within a preform, so as to yield a dense composite with a high ceramic content. In the present case, a displacement reaction between liquid Mg and solid Alsb2Osb3 was used to produce composites of MgO and Mg-bearing metal. Porous, shaped Alsb2Osb3 preforms were placed in contact with a Mg(l) bath at 1000sp°C. The liquid Mg completely infiltrated and consumed the Alsb2Osb3 preform by the following net reaction:$3Mg(l) + Alsb2

  14. Shape, Loading, and Motion in the Bioengineering Design, Fabrication, and Testing of Personalized Synovial Joints

    PubMed Central

    Williams, Gregory M.; Chan, Elaine F.; Temple-Wong, Michele M.; Bae, Won C.; Masuda, Koichi; Bugbee, William D.; Sah, Robert L.

    2009-01-01

    With continued development and improvement of tissue engineering therapies for small articular lesions, increased attention is being focused on the challenge of engineering partial or whole synovial joints. Joint-scale constructs could have applications in the treatment of large areas of articular damage or in biological arthroplasty of severely degenerate joints. This review considers the roles of shape, loading and motion in synovial joint mechanobiology and their incorporation into the design, fabrication, and testing of engineered partial or whole joints. Incidence of degeneration, degree of impairment, and efficacy of current treatments are critical factors in choosing a target for joint bioengineering. The form and function of native joints may guide the design of engineered joint-scale constructs with respect to size, shape, and maturity. Fabrication challenges for joint-scale engineering include controlling chemo-mechano-biological microenvironments to promote the development and growth of multiple tissues with integrated interfaces or lubricated surfaces into anatomical shapes, and joint-scale bioreactors which nurture and stimulate the tissue with loading and motion. Finally, evaluation of load-bearing and tribological properties can range from tissue to joint scale and can focus on biological structure at present or after adaptation. PMID:19815214

  15. Convoluted accommodation structures in folded rocks

    NASA Astrophysics Data System (ADS)

    Dodwell, T. J.; Hunt, G. W.

    2012-10-01

    A simplified variational model for the formation of convoluted accommodation structures, as seen in the hinge zones of larger-scale geological folds, is presented. The model encapsulates some important and intriguing nonlinear features, notably: infinite critical loads, formation of plastic hinges, and buckling on different length-scales. An inextensible elastic beam is forced by uniform overburden pressure and axial load into a V-shaped geometry dictated by formation of a plastic hinge. Using variational methods developed by Dodwell et al., upon which this paper leans heavily, energy minimisation leads to representation as a fourth-order nonlinear differential equation with free boundary conditions. Equilibrium solutions are found using numerical shooting techniques. Under the Maxwell stability criterion, it is recognised that global energy minimisers can exist with convoluted physical shapes. For such solutions, parallels can be drawn with some of the accommodation structures seen in exposed escarpments of real geological folds.

  16. Free form fabrication using the laser engineered net shaping (LENS{trademark}) process

    SciTech Connect

    Keicher, D.M.; Romero, J.A.; Atwood, C.L.; Griffith, M.L.; Jeantette, F.P.; Harwell, L.D.; Greene, D.L.; Smugeresky, J.E.

    1996-12-31

    Sandia National Laboratories is developing a technology called Laser Engineered Net Shaping{trademark} (LENS{trademark}). This process allows complex 3-dimensional solid metallic objects to be directly fabricated for a CAD solid model. Experiments performed demonstrate that complex alloys such as Inconel{trademark} 625 and ANSI stainless steel alloy 316 can be used in the LENS{trademark} process to produce solid metallic-shapes. In fact, the fabricated structures exhibit grain growth across the deposition layer boundaries. Mechanical testing data of deposited 316 stainless steel material indicates that the deposited material strength and elongation are greater than that reported for annealed 316 stainless steel. Electron microprobe analysis of the deposited Inconel{trademark} 625 material shows no compositional degradation of the 625 alloy and that 100% dense structures can be obtained using this technique. High speed imaging used to acquire process data during experimentation shows that the powder particle size range can significantly affect the stability, and subsequently, the performance of the powder deposition process. Finally, dimensional studies suggest that dimensional accuracy to {+-} 0.002 inches (in the horizontal direction) can be maintained.

  17. Fabrication and In Vitro Deployment of a Laser-Activated Shape Memory Polymer Vascular Stent

    SciTech Connect

    Baer, G M; Small IV, W; Wilson, T S; Benett, W J; Matthews, D L; Hartman, J; Maitland, D J

    2007-04-25

    Vascular stents are small tubular scaffolds used in the treatment of arterial stenosis (narrowing of the vessel). Most vascular stents are metallic and are deployed either by balloon expansion or by self-expansion. A shape memory polymer (SMP) stent may enhance flexibility, compliance, and drug elution compared to its current metallic counterparts. The purpose of this study was to describe the fabrication of a laser-activated SMP stent and demonstrate photothermal expansion of the stent in an in vitro artery model. A novel SMP stent was fabricated from thermoplastic polyurethane. A solid SMP tube formed by dip coating a stainless steel pin was laser-etched to create the mesh pattern of the finished stent. The stent was crimped over a fiber-optic cylindrical light diffuser coupled to an infrared diode laser. Photothermal actuation of the stent was performed in a water-filled mock artery. At a physiological flow rate, the stent did not fully expand at the maximum laser power (8.6 W) due to convective cooling. However, under zero flow, simulating the technique of endovascular flow occlusion, complete laser actuation was achieved in the mock artery at a laser power of {approx}8 W. We have shown the design and fabrication of an SMP stent and a means of light delivery for photothermal actuation. Though further studies are required to optimize the device and assess thermal tissue damage, photothermal actuation of the SMP stent was demonstrated.

  18. Fabrication and characterization of graphitic carbon nanostructures with controllable size, shape, and position.

    PubMed

    Du, Rongbing; Ssenyange, Solomon; Aktary, Mirwais; McDermott, Mark T

    2009-05-01

    The incorporation of carbon materials in micro- and nanoscale devices is being widely investigated due to the promise of enhanced functionality. Challenges in the positioning and addressability of carbon nanotubes provide the motivation for the development of new processes to produce nanoscale carbon materials. Here, the fabrication of conducting, nanometer-sized carbon structures using a combination of electron beam lithography (EBL) and carbonisation is reported. EBL is used to directly write predefined nanometer-sized patterns in a thin layer of negative resist in controllable locations. Careful heat treatment results in carbon nanostructures with the size, shape, and location originally defined by EBL. The pyrolysis process results in significant shrinkage of the structures in the vertical direction and minimal loss in the horizontal direction. Characterization of the carbonized material indicates a structure consisting of both amorphous and graphitized carbon with low levels of oxygen. The resistivity of the material is similar to other disordered carbon materials and the resistivity is maintained from the bulk to the nanoscale. This is demonstrated by fabricating a nanoscale structure with predictable resistance. The ability to fabricate these conductive structures with known dimensions and in predefined locations can be exploited for a number of applications. Their use as nanoband electrodes is also demonstrated.

  19. Facile moldless fabrication of disk-shaped and reed blood cell-like microparticles using photopolymerization of tripropylene glycol diacrylate

    NASA Astrophysics Data System (ADS)

    Choi, Jongchul; Won, June; Song, Simon

    2014-12-01

    A facile method for the moldless fabrication of 2- or 3-dimensional microparticles is proposed by using a photopolymerization technique. Using only a monomer solution of tripropylene glycol diacrylate, a film mask and standard UV lithography equipment, we were able to fabricate microparticles of various shapes, such as disks, dimpled disks similar in shape to red blood cells, and slender gourd shapes, unlike previous moldless fabrication techniques requiring expensive and/or sophisticated equipment. The simple method could produce more than one million particles in a single batch, indicating that it can be applied to the mass production of polymer microparticles. Analyses of scanning electron micrographs and optical micrographs of the microparticles indicated that their size distribution was highly monodisperse. Detailed fabrication processes and statistics on the microparticle sizes are given in this paper.

  20. A fabrication method of unique Nafion® shapes by painting for ionic polymer-metal composites

    NASA Astrophysics Data System (ADS)

    Trabia, Sarah; Hwang, Taeseon; Kim, Kwang J.

    2016-08-01

    Ionic polymer-metal composites (IPMC) are useful actuators because of their ability to be fabricated in different shapes and move in various ways. However, producing unique or intricate shapes can be difficult based upon the current fabrication techniques. Presented here is a fabrication method of producing the Nafion® membrane or thin film through a painting method. Using an airbrush, a Nafion water dispersion is sprayed onto an acrylonitrile butadiene styrene surface with a stencil of the desired shape. To verify that this method of fabrication produces a Nafion membrane similar to that which is commercially available, a sample that was made using the painting method and Nafion 117 purchased from DuPont™ were tested for various characteristics and compared. The results show promising similarities. The painted Nafion sample was chemically plated with platinum and compared with a traditional IPMC for its displacement and blocking force capabilities. The painted IPMC sample showed comparable results.

  1. Design and fabrication of uniquely shaped thiol-ene microfibers using a two-stage hydrodynamic focusing design.

    PubMed

    Boyd, Darryl A; Shields, Adam R; Howell, Peter B; Ligler, Frances S

    2013-08-07

    Microfluidic systems have advantages that are just starting to be realized for materials fabrication. In addition to the more common use for fabrication of particles, hydrodynamic focusing has been used to fabricate continuous polymer fibers. We have previously described such a microfluidics system which has the ability to generate fibers with controlled cross-sectional shapes locked in place by in situ photopolymerization. The previous fiber fabrication studies produced relatively simple round or ribbon shapes, demonstrated the use of a variety of polymers, and described the interaction between sheath-core flow-rate ratios used to control the fiber diameter and the impact on possible shapes. These papers documented the fact that no matter what the intended shape, higher flow-rate ratios produced rounder fibers, even in the absence of interfacial tension between the core and sheath fluids. This work describes how to fabricate the next generation of fibers predesigned to have a much more complex geometry, as exemplified by the "double anchor" shape. Critical to production of the pre-specified fibers with complex features was independent control over both the shape and the size of the fabricated microfibers using a two-stage hydrodynamic focusing system. Design and optimization of the channels was performed using finite element simulations and confocal imaging to characterize each of the two stages theoretically and experimentally. The resulting device design was then used to generate thiol-ene fibers with a unique double anchor shape. Finally, proof-of-principle functional experiments demonstrated the ability of the fibers to transport fluids and to interlock laterally.

  2. Lipid Nanotube Tailored Fabrication of Uniquely Shaped Polydopamine Nanofibers as Photothermal Converters.

    PubMed

    Ding, Wuxiao; Chechetka, Svetlana A; Masuda, Mitsutoshi; Shimizu, Toshimi; Aoyagi, Masaru; Minamikawa, Hiroyuki; Miyako, Eijiro

    2016-03-18

    Helically coiled and linear polydopamine (PDA) nanofibers were selectively fabricated with two different types of lipid nanotubes (LNTs) that acted as templates. The obtained coiled PDA-LNT hybrid showed morphological advantages such as higher light absorbance and photothermal conversion effect compared to a linear counterpart. Laser irradiation of the coiled PDA-LNT hybrid induced a morphological change and subsequent release of the encapsulated guest molecule. In cellular experiments, the coiled PDA-LNT efficiently eliminated HeLa cells because of its strong affinity with the tumor cells. This work illustrates the first approach to construct characteristic morphologies of PDA nanofibers using LNTs as simple templates, and the coiled PDA-LNT hybrid exhibits attractive photothermal features derived from its unique coiled shape.

  3. Fabrication of shape controlled Fe{sub 3}O{sub 4} nanostructure

    SciTech Connect

    Zheng, Y.Y.; Wang, X.B.; Shang, L.; Li, C.R.; Cui, C.; Dong, W.J.; Tang, W.H.; Chen, B.Y.

    2010-04-15

    Shape-controlled Fe{sub 3}O{sub 4} nanostructure has been successfully prepared using polyethylene glycol as template in a water system at room temperature. Different morphologies of Fe{sub 3}O{sub 4} nanostructures, including spherical, cubic, rod-like, and dendritic nanostructure, were obtained by carefully controlling the concentration of the Fe{sup 3+}, Fe{sup 2+}, and the molecular weight of the polyethylene glycol. Transmission Electron Microscope images, X-ray powder diffraction patterns and magnetic properties were used to characterize the final product. This easy procedure for Fe{sub 3}O{sub 4} nanostructure fabrication offers the possibility of a generalized approach to the production of single and complex nanocrystalline oxide with tunable morphology.

  4. Fabrication and Characterization of Cylindrical Light Diffusers Comprised of Shape Memory Polymer

    SciTech Connect

    Small IV, W; Buckley, P R; Wilson, T S; Loge, J M; Maitland, K D; Maitland, D J

    2007-01-29

    We have developed a technique for constructing light diffusing devices comprised of a flexible shape memory polymer (SMP) cylindrical diffuser attached to the tip of an optical fiber. Devices were fabricated by casting an SMP rod over the cleaved tip of an optical fiber and media blasting the SMP rod to create a light diffusing surface. The axial and polar emission profiles and circumferential (azimuthal) uniformity were characterized for various blasting pressures, nozzle-to-sample distances, and nozzle translation speeds. The diffusers were generally strongly forward-directed and consistently withstood over 8 W of incident infrared laser light without suffering damage when immersed in water. These devices are suitable for various endoluminal and interstitial biomedical applications.

  5. WFIRST-AFTA coronagraph shaped pupil masks: design, fabrication, and characterization

    NASA Astrophysics Data System (ADS)

    Balasubramanian, Kunjithapatham; White, Victor; Yee, Karl; Echternach, Pierre; Muller, Richard; Dickie, Matthew; Cady, Eric; Prada, Camilo Mejia; Ryan, Daniel; Poberezhskiy, Ilya; Kern, Brian; Zhou, Hanying; Krist, John; Nemati, Bijan; Eldorado Riggs, A. J.; Zimmerman, Neil T.; Kasdin, N. Jeremy

    2016-01-01

    NASA WFIRST-AFTA mission study includes a coronagraph instrument to find and characterize exoplanets. Various types of masks could be employed to suppress the host starlight to about 10-9 level contrast over a broad spectrum to enable the coronagraph mission objectives. Such masks for high-contrast internal coronagraphic imaging require various fabrication technologies to meet a wide range of specifications, including precise shapes, micron scale island features, ultralow reflectivity regions, uniformity, wave front quality, and achromaticity. We present the approaches employed at JPL to produce pupil plane and image plane coronagraph masks by combining electron beam, deep reactive ion etching, and black silicon technologies with illustrative examples of each, highlighting milestone accomplishments from the High Contrast Imaging Testbed at JPL and from the High Contrast Imaging Lab at Princeton University.

  6. Shape-Controlled Fabrication of the Polymer-Based Micromotor Based on the Polydimethylsiloxane Template.

    PubMed

    Su, Miaoda; Liu, Mei; Liu, Limei; Sun, Yunyu; Li, Mingtong; Wang, Dalei; Zhang, Hui; Dong, Bin

    2015-11-03

    We report the utilization of the polydimethylsiloxane template to construct polymer-based autonomous micromotors with various structures. Solid or hollow micromotors, which consist of polycaprolactone and platinum nanoparticles, can be obtained with controllable sizes and shapes. The resulting micromotor can not only be self-propelled in solution based on the bubble propulsion mechanism in the presence of the hydrogen peroxide fuel, but also exhibit structure-dependent motion behavior. In addition, the micromotors can exhibit various functions, ranging from fluorescence, magnetic control to cargo transportation. Since the current method can be extended to a variety of organic and inorganic materials, we thus believe it may have great potential in the fabrication of different functional micromotors for diverse applications.

  7. Facile 3D Metal Electrode Fabrication for Energy Applications via Inkjet Printing and Shape Memory Polymer

    NASA Astrophysics Data System (ADS)

    Roberts, R. C.; Wu, J.; Hau, N. Y.; Chang, Y. H.; Feng, S. P.; Li, D. C.

    2014-11-01

    This paper reports on a simple 3D metal electrode fabrication technique via inkjet printing onto a thermally contracting shape memory polymer (SMP) substrate. Inkjet printing allows for the direct patterning of structures from metal nanoparticle bearing liquid inks. After deposition, these inks require thermal curing steps to render a stable conductive film. By printing onto a SMP substrate, the metal nanoparticle ink can be cured and substrate shrunk simultaneously to create 3D metal microstructures, forming a large surface area topology well suited for energy applications. Polystyrene SMP shrinkage was characterized in a laboratory oven from 150-240°C, resulting in a size reduction of 1.97-2.58. Silver nanoparticle ink was patterned into electrodes, shrunk, and the topology characterized using scanning electron microscopy. Zinc-Silver Oxide microbatteries were fabricated to demonstrate the 3D electrodes compared to planar references. Characterization was performed using 10M potassium hydroxide electrolyte solution doped with zinc oxide (57g/L). After a 300s oxidation at 3Vdc, the 3D electrode battery demonstrated a 125% increased capacity over the reference cell. Reference cells degraded with longer oxidations, but the 3D electrodes were fully oxidized for 4 hours, and exhibited a capacity of 5.5mA-hr/cm2 with stable metal performance.

  8. Exoplanet coronagraph shaped pupil masks and laboratory scale star shade masks: design, fabrication and characterization

    NASA Astrophysics Data System (ADS)

    Balasubramanian, Kunjithapatham; White, Victor; Yee, Karl; Echternach, Pierre; Muller, Richard; Dickie, Matthew; Cady, Eric; Mejia Prada, Camilo; Ryan, Daniel; Poberezhskiy, Ilya; Zhou, Hanying; Kern, Brian; Riggs, A. J.; Zimmerman, Neil T.; Sirbu, Dan; Shaklan, Stuart; Kasdin, Jeremy

    2015-09-01

    Star light suppression technologies to find and characterize faint exoplanets include internal coronagraph instruments as well as external star shade occulters. Currently, the NASA WFIRST-AFTA mission study includes an internal coronagraph instrument to find and characterize exoplanets. Various types of masks could be employed to suppress the host star light to about 10-9 level contrast over a broad spectrum to enable the coronagraph mission objectives. Such masks for high contrast internal coronagraphic imaging require various fabrication technologies to meet a wide range of specifications, including precise shapes, micron scale island features, ultra-low reflectivity regions, uniformity, wave front quality, achromaticity, etc. We present the approaches employed at JPL to produce pupil plane and image plane coronagraph masks by combining electron beam, deep reactive ion etching, and black silicon technologies with illustrative examples of each, highlighting milestone accomplishments from the High Contrast Imaging Testbed (HCIT) at JPL and from the High Contrast Imaging Lab (HCIL) at Princeton University. We also present briefly the technologies applied to fabricate laboratory scale star shade masks.

  9. Fabrication and Characterization of Nitinol-Copper Shape Memory Alloy Bimorph Actuators

    NASA Astrophysics Data System (ADS)

    Wongweerayoot, E.; Srituravanich, W.; Pimpin, A.

    2015-02-01

    This study aims to examine the effect of annealing conditions on nitinol (NiTi) characteristics and applies this knowledge to fabricate a NiTi-copper shape memory alloy bimorph actuator. The effect of the annealing conditions was investigated at various temperatures, i.e., 500, 600, and 650 °C, for 30 min. With the characterizations using x-ray diffraction, energy dispersive spectroscopy, and differential scanning calorimetry techniques, the results showed that annealing temperatures at 600 and 650 °C were able to appropriately form the crystalline structure of NiTi. However, at these high annealing temperatures, the oxide on a surface was unavoidable. In the fabrication of actuator, the annealing at 650 °C for 30 min was chosen, and it was performed at two pre-stressing conditions, i.e., straight and curved molds. From static and dynamic response experiments, the results suggested that the annealing temperature significantly affected the deflection of the actuator. On the other hand, the effect of pre-stressing conditions was relatively small. Furthermore, the micro gripper consisting of two NiTi-copper bimorph actuators successfully demonstrated for the viability of small object manipulation as the gripper was able to grasp and hold a small plastic ball with its weight of around 0.5 mg.

  10. Near-Net Shape Fabrication Using Low-Cost Titanium Alloy Powders

    SciTech Connect

    Dr. David M. Bowden; Dr. William H. Peter

    2012-03-31

    The use of titanium in commercial aircraft production has risen steadily over the last half century. The aerospace industry currently accounts for 58% of the domestic titanium market. The Kroll process, which has been used for over 50 years to produce titanium metal from its mineral form, consumes large quantities of energy. And, methods used to convert the titanium sponge output of the Kroll process into useful mill products also require significant energy resources. These traditional approaches result in product forms that are very expensive, have long lead times of up to a year or more, and require costly operations to fabricate finished parts. Given the increasing role of titanium in commercial aircraft, new titanium technologies are needed to create a more sustainable manufacturing strategy that consumes less energy, requires less material, and significantly reduces material and fabrication costs. A number of emerging processes are under development which could lead to a breakthrough in extraction technology. Several of these processes produce titanium alloy powder as a product. The availability of low-cost titanium powders may in turn enable a more efficient approach to the manufacture of titanium components using powder metallurgical processing. The objective of this project was to define energy-efficient strategies for manufacturing large-scale titanium structures using these low-cost powders as the starting material. Strategies include approaches to powder consolidation to achieve fully dense mill products, and joining technologies such as friction and laser welding to combine those mill products into near net shape (NNS) preforms for machining. The near net shape approach reduces material and machining requirements providing for improved affordability of titanium structures. Energy and cost modeling was used to define those approaches that offer the largest energy savings together with the economic benefits needed to drive implementation. Technical

  11. Convolutional coding techniques for data protection

    NASA Technical Reports Server (NTRS)

    Massey, J. L.

    1975-01-01

    Results of research on the use of convolutional codes in data communications are presented. Convolutional coding fundamentals are discussed along with modulation and coding interaction. Concatenated coding systems and data compression with convolutional codes are described.

  12. An innovative method and experiment for fabricating bulgy shape nanochannel using AFM

    NASA Astrophysics Data System (ADS)

    Lin, Zone-Ching; Jheng, Hao-Yuan; Ding, Hao-Yang

    2015-08-01

    The paper proposes using atomic force microscopy (AFM) and the concept of specific down force energy (SDFE) to establish an innovative offset cycle cutting method for fabricating a bulgy shape nanochannel on a single-crystal silicon substrate. In the offset cycle cutting method, cutting is performed at a constant down force in all cutting passes. After the first cutting pass, the AFM probe is offset rightward for the second pass and subsequently offset leftward to the middle (i.e., between the positions of the first two cutting passes) for the third cutting pass. Applying a step-by-step method to modify the offset distance and approach the defined SDFE value, this study determined the depth of the middle cutting pass and smaller values of upward bulginess and downward indentation at the bottom of the nanochannel. The nanochannel width can be increased by increasing the number of offset cycle cutting passes. In addition, by applying the proposed method, this study involved a simulation and experiment concerning the cutting path plan of bulgy shape nanochannels. Furthermore, using a small down force along the burr path is proposed for reducing burr height. The results of the simulation and experiment were compared to verify the feasibility of the method.

  13. Fabrication and lithium storage performance of sugar apple-shaped SiOx@C nanocomposite spheres

    NASA Astrophysics Data System (ADS)

    Li, Mingqi; Zeng, Ying; Ren, Yurong; Zeng, Chunmei; Gu, Jingwei; Feng, Xiaofang; He, Hongyan

    2015-08-01

    Nonstoichiometric SiOx is a kind of very attractive anode material for high-energy lithium-ion batteries because of a high specific capacity and facile synthesis. However, the poor electrical conductivity and unstable electrode structure of SiOx severely limit its electrochemical performance as anode in lithium-ion batteries. In this work, highly durable sugar apple-shaped SiOx@C nanocomposite spheres are fabricated to achieve significantly improved electrochemical performance. The composite is synthesized by homogenous one-pot synthesis, using ethyltriethoxysilanes (EtSi(OEt)3) and resorcinol/formaldehyde (RF) as starting materials. The morphology, composition and structure of the composite are investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental analysis (EA) and X-ray photoelectron spectroscopy (XPS). At a current density of 50 mA g-1, the sugar apple-shaped SiOx@C spheres exhibit a stable discharge capacity of about 630 mAh g-1 calculated on the total mass of both SiOx and C. At a current density of 100 mA g-1, a stable discharge capacity of about 550 mAh g-1 is obtained and the capacity has been kept up to 400 cycles. The excellent cycling performance is attributed to the homogeneous dispersion of SiOx in disordered carbon at the nanometer scale and the unique structure of the composite.

  14. Glass-on-Glass Fabrication of Bottle-Shaped Tunable Microlasers and their Applications.

    PubMed

    Ward, Jonathan M; Yang, Yong; Nic Chormaic, Síle

    2016-04-28

    We describe a novel method for making microbottle-shaped lasers by using a CO2 laser to melt Er:Yb glass onto silica microcapillaries or fibres. This is realised by the fact that the two glasses have different melting points. The CO2 laser power is controlled to flow the doped glass around the silica cylinder. In the case of a capillary, the resulting geometry is a hollow, microbottle-shaped resonator. This is a simple method for fabricating a number of glass whispering gallery mode (WGM) lasers with a wide range of sizes on a single, micron-scale structure. The Er:Yb doped glass outer layer is pumped at 980 nm via a tapered optical fibre and WGM lasing is recorded around 1535 nm. This structure facilitates a new way to thermo-optically tune the microlaser modes by passing gas through the capillary. The cooling effect of the gas flow shifts the WGMs towards shorter wavelengths and thermal tuning of the lasing modes over 70 GHz is achieved. Results are fitted using the theory of hot wire anemometry, allowing the flow rate to be calibrated with a flow sensitivity as high as 72 GHz/sccm. Strain tuning of the microlaser modes by up to 60 GHz is also demonstrated.

  15. Glass-on-Glass Fabrication of Bottle-Shaped Tunable Microlasers and their Applications

    NASA Astrophysics Data System (ADS)

    Ward, Jonathan M.; Yang, Yong; Nic Chormaic, Síle

    2016-04-01

    We describe a novel method for making microbottle-shaped lasers by using a CO2 laser to melt Er:Yb glass onto silica microcapillaries or fibres. This is realised by the fact that the two glasses have different melting points. The CO2 laser power is controlled to flow the doped glass around the silica cylinder. In the case of a capillary, the resulting geometry is a hollow, microbottle-shaped resonator. This is a simple method for fabricating a number of glass whispering gallery mode (WGM) lasers with a wide range of sizes on a single, micron-scale structure. The Er:Yb doped glass outer layer is pumped at 980 nm via a tapered optical fibre and WGM lasing is recorded around 1535 nm. This structure facilitates a new way to thermo-optically tune the microlaser modes by passing gas through the capillary. The cooling effect of the gas flow shifts the WGMs towards shorter wavelengths and thermal tuning of the lasing modes over 70 GHz is achieved. Results are fitted using the theory of hot wire anemometry, allowing the flow rate to be calibrated with a flow sensitivity as high as 72 GHz/sccm. Strain tuning of the microlaser modes by up to 60 GHz is also demonstrated.

  16. Glass-on-Glass Fabrication of Bottle-Shaped Tunable Microlasers and their Applications

    PubMed Central

    Ward, Jonathan M.; Yang, Yong; Nic Chormaic, Síle

    2016-01-01

    We describe a novel method for making microbottle-shaped lasers by using a CO2 laser to melt Er:Yb glass onto silica microcapillaries or fibres. This is realised by the fact that the two glasses have different melting points. The CO2 laser power is controlled to flow the doped glass around the silica cylinder. In the case of a capillary, the resulting geometry is a hollow, microbottle-shaped resonator. This is a simple method for fabricating a number of glass whispering gallery mode (WGM) lasers with a wide range of sizes on a single, micron-scale structure. The Er:Yb doped glass outer layer is pumped at 980 nm via a tapered optical fibre and WGM lasing is recorded around 1535 nm. This structure facilitates a new way to thermo-optically tune the microlaser modes by passing gas through the capillary. The cooling effect of the gas flow shifts the WGMs towards shorter wavelengths and thermal tuning of the lasing modes over 70 GHz is achieved. Results are fitted using the theory of hot wire anemometry, allowing the flow rate to be calibrated with a flow sensitivity as high as 72 GHz/sccm. Strain tuning of the microlaser modes by up to 60 GHz is also demonstrated. PMID:27121151

  17. Determinate-state convolutional codes

    NASA Technical Reports Server (NTRS)

    Collins, O.; Hizlan, M.

    1991-01-01

    A determinate state convolutional code is formed from a conventional convolutional code by pruning away some of the possible state transitions in the decoding trellis. The type of staged power transfer used in determinate state convolutional codes proves to be an extremely efficient way of enhancing the performance of a concatenated coding system. The decoder complexity is analyzed along with free distances of these new codes and extensive simulation results is provided of their performance at the low signal to noise ratios where a real communication system would operate. Concise, practical examples are provided.

  18. Experimental characterisation of PD SOI MOSFET devices fabricated with diamond-shaped body contact

    NASA Astrophysics Data System (ADS)

    Daghighi, Arash; Osman, Mohamed A.

    2011-06-01

    The design of diamond-shaped body-contacted (DSBC) devices using standard layers in a 0.35 µm silicon-on-insulator (SOI) complementary metal-oxide-semiconductor process is described in this article. The technology is based on a manufacturable partially depleted SOI process targeted for radio frequency applications. The experimental measurements of drain induced barrier lowering for the fabricated DSBC structure showed suppression of floating body effects (FBE) at the promising rate of 24 mV/V. The measurement results confirmed current drive (I DS) improvement by 25% at V DS = 1.5 V and V GS = 1.5 V compared to conventional body-tied-source (BTS) device. A constant and steady output conductance (g DS) in the saturation region was observed for the DSBC structure. The gate trans-conductance (g m) is improved by 34% at V DS = 1.5 V and V GS = 1.5 V compared to conventional BTS device. Three-dimensional device simulation provides insight on FBE suppression and channel current improvement. Experimental results confirmed the area efficiency of the DSBC structure and its excellent current drive performance.

  19. A Novel Shape Memory Alloy Annuloplasty Ring for Minimally Invasive Surgery: Design, Fabrication, and Evaluation

    PubMed Central

    Purser, Molly F.; Richards, Andrew L.; Cook, Richard C.; Osborne, Jason A.; Cormier, Denis R.; Buckner, Gregory D.

    2013-01-01

    A novel annuloplasty ring with a shape memory alloy core has been developed to facilitate minimally invasive mitral valve repair. In its activated (austenitic) phase, this prototype ring has comparable mechanical properties to commercial semi-rigid rings. In its pre-activated (martensitic) phase, this ring is flexible enough to be introduced through an 8-mm trocar and easily manipulated with robotic instruments within the confines of a left atrial model. The core is constructed of 0.50 mm diameter NiTi, which is maintained below its martensitic transition temperature (24 °C) during deployment and suturing. After suturing, the ring is heated above its austenitic transition temperature (37 °C, normal human body temperature) enabling the NiTi core to attain its optimal geometry and stiffness characteristics indefinitely. This article summarizes the design, fabrication, and evaluation of this prototype ring. Experimental results suggest that the NiTi core ring could be a viable alternative to flexible bands in robot-assisted minimally invasive mitral valve repair. PMID:20652747

  20. Polarization-independent etching of fused silica based on electrons dynamics control by shaped femtosecond pulse trains for microchannel fabrication.

    PubMed

    Yan, X; Jiang, L; Li, X; Zhang, K; Xia, B; Liu, P; Qu, L; Lu, Y

    2014-09-01

    We propose an approach to realize polarization-independent etching of fused silica by using temporally shaped femtosecond pulse trains to control the localized transient electrons dynamics. Instead of nanograting formation using traditional unshaped pulses, for the pulse delay of pulse trains larger than 1 ps, coherent field-vector-related coupling is not possible and field orientation is lost. The exponential growth of the periodic structures is interrupted. In this case, disordered and interconnected nanostructures are formed, which is probably the main reason of etching independence on the laser polarization. As an application example, square-wave-shaped and arc-shaped microchannels are fabricated by using pulse trains to demonstrate the advantage of the proposed method in fabricating high-aspect-ratio and three-dimensional microchannels.

  1. Fabrication of 10 nm-scale complex 3D nanopatterns with multiple shapes and components by secondary sputtering phenomenon.

    PubMed

    Jeon, Hwan-Jin; Jeong, Hyeon Su; Kim, Yun Ho; Jung, Woo-Bin; Kim, Jeong Yeon; Jung, Hee-Tae

    2014-02-25

    We introduce an advanced ultrahigh-resolution (∼ 15 nm) patterning technique that enables the fabrication of various 3D high aspect ratio multicomponents/shaped nanostructures. This methodology utilizes the repetitive secondary sputtering phenomenon under etching plasma conditions and prepatterned fabrication control. The secondary sputtering phenomenon repetitively generates an angular distribution of target particles during ion-bombardment. This method, advanced repetitive secondary sputtering lithography, provides many strategies to fabricate complex continuous patterns and multilayer/material patterns with 10 nm-scale resolution. To demonstrate the versatility of this method, we show induced vertical alignment of liquid crystals (LCs) on indium-tin-oxide (ITO) grid patterns without any alignment layers. The ITO grid pattern fabricated in this method is found to have not only an alignment capability but also electrode properties without electrical or optical damage.

  2. Melt-spun shaped fibers with enhanced surface effects: fiber fabrication, characterization and application to woven scaffolds.

    PubMed

    Park, S J; Lee, B-K; Na, M H; Kim, D S

    2013-08-01

    Scaffolds with a high surface-area-to-volume ratio (SA:V) are advantageous with regard to the attachment and proliferation of cells in the field of tissue engineering. This paper reports on the development of novel melt-spun fibers with a high SA:V, which enhanced the surface effects of a fiber-based scaffold while maintaining its mechanical strength. The cross-section of the fibers was altered to a non-circular shape, producing a higher SA:V for a similar cross-sectional area. To obtain fibers with non-circular cross-sectional shape, or shaped fibers, three different types of metal spinnerets were fabricated for the melt-spinning process, each with circular, triangular or cruciform capillaries, using deep X-ray lithography followed by nickel electroforming. Using these spinnerets, circular and shaped fibers were manufactured with biodegradable polyester, polycaprolactone. The SA:V increase in the shaped fibers was experimentally investigated under different processing conditions. Tensile tests on the fibers and indentation tests on the woven fiber scaffolds were performed. The tested fibers and scaffolds exhibited similar mechanical characteristics, due to the similar cross-sectional area of the fibers. The degradation of the shaped fibers was notably faster than that of circular fibers, because of the enlarged surface area of the shaped fibers. The woven scaffolds composed of the shaped fibers significantly increased the proliferation of human osteosarcoma MG63 cells. This approach to increase the SA:V in shaped fibers could be useful for the fabrication of programmable, biodegradable fiber-based scaffolds in tissue engineering.

  3. Die and telescoping punch form convolutions in thin diaphragm

    NASA Technical Reports Server (NTRS)

    1965-01-01

    Die and punch set forms convolutions in thin dished metal diaphragm without stretching the metal too thin at sharp curvatures. The die corresponds to the metal shape to be formed, and the punch consists of elements that progressively slide against one another under the restraint of a compressed-air cushion to mate with the die.

  4. Fabrication of volcano-shaped nano-patterned sapphire substrates using colloidal self-assembly and wet chemical etching.

    PubMed

    Geng, Chong; Zheng, Lu; Fang, Huajing; Yan, Qingfeng; Wei, Tongbo; Hao, Zhibiao; Wang, Xiaoqing; Shen, Dezhong

    2013-08-23

    Patterned sapphire substrates (PSS) have been widely used to enhance the light output power in GaN-based light emitting diodes. The shape and feature size of the pattern in a PSS affect its enhancement efficiency to a great degree. In this work we demonstrate the nanoscale fabrication of volcano-shaped PSS using a wet chemical etching approach in combination with a colloidal monolayer templating strategy. Detailed analysis by scanning electron microscopy reveals that the unique pattern shape is a result of the different corrosion-resistant abilities of silica masks of different effective heights during wet chemical etching. The formation of silica etching masks of different effective heights has been ascribed to the silica precursor solution in the interstice of the colloidal monolayer template being distributed unevenly after infiltration. In the subsequent wet chemical etching process, the active reaction sites altered as etching duration was prolonged, resulting in the formation of volcano-shaped nano-patterned sapphire substrates.

  5. Slit beam shaping method for femtosecond laser direct-write fabrication of symmetric waveguides in bulk glasses

    NASA Astrophysics Data System (ADS)

    Ams, Martin; Marshall, G. D.; Spence, D. J.; Withford, M. J.

    2005-07-01

    We report both theoretical and experimental results of a slit beam shaping configuration for fabricating photonic waveguides by use of femtosecond laser pulses. Most importantly we show the method supports focusing objectives with a long depth of field and allows the direct-writing of microstructures with circular cross-sections whilst employing a perpendicular writing scheme. We applied this technique to write low loss (0.39 dB/cm), single mode waveguides in phosphate glass.

  6. Fabrication of petal-shaped masks for suppression of the on-axis Poisson spot in telescope systems.

    PubMed

    Shiri, Ron; Stein, Ryan; Murphy, Kaitlin; Hagopian, Kimberly; Salari, Shirin; Sankar, Shannon; Hagopian, John; Showalter, Matthew; Stevenson, Thomas; Quijada, Manuel; Threat, Felix; Friedlander, Jay; Dillon, Thomas; Livas, Jeffrey

    2016-04-01

    The presence of a bright (Poisson) spot in the geometrical shadow of circular/spherical shapes has been known for the past two centuries. A broad class of telescopes that involve simultaneous transmit and receive require suppression of the reflected light from the secondary mirror on the detector. For instance, the on-axis design of optical telescope for the evolved Laser Interferometric Space Antenna (eLISA), a re-scoped version of the baseline LISA mission concept, requires suppression of reflected laser light from the secondary mirror on the detector. In the past few years, the hypergaussian functions with petal-shaped realization have been shown to significantly suppress intensity along the optical axis. This work reports on fabrication of a series of petal-shaped masks using a variety of techniques such as 3D printing, photolithography, and wire Electro Discharge Machining. These masks are designed and fabricated to operate in the range of Fresnel numbers between 4 and 120. This paper discusses the challenges, successes, and failures of each fabrication technique and the optical performance of typical masks with suggestions for potential follow up work.

  7. Fabrication of petal-shaped masks for suppression of the on-axis Poisson spot in telescope systems

    NASA Astrophysics Data System (ADS)

    Shiri, Ron; Stein, Ryan; Murphy, Kaitlin; Hagopian, Kimberly; Salari, Shirin; Sankar, Shannon; Hagopian, John; Showalter, Matthew; Stevenson, Thomas; Quijada, Manuel; Threat, Felix; Friedlander, Jay; Dillon, Thomas; Livas, Jeffrey

    2016-04-01

    The presence of a bright (Poisson) spot in the geometrical shadow of circular/spherical shapes has been known for the past two centuries. A broad class of telescopes that involve simultaneous transmit and receive require suppression of the reflected light from the secondary mirror on the detector. For instance, the on-axis design of optical telescope for the evolved Laser Interferometric Space Antenna (eLISA), a re-scoped version of the baseline LISA mission concept, requires suppression of reflected laser light from the secondary mirror on the detector. In the past few years, the hypergaussian functions with petal-shaped realization have been shown to significantly suppress intensity along the optical axis. This work reports on fabrication of a series of petal-shaped masks using a variety of techniques such as 3D printing, photolithography, and wire Electro Discharge Machining. These masks are designed and fabricated to operate in the range of Fresnel numbers between 4 and 120. This paper discusses the challenges, successes, and failures of each fabrication technique and the optical performance of typical masks with suggestions for potential follow up work.

  8. Fabrication and Characterization of a Micromachined Swirl-Shaped Ionic Polymer Metal Composite Actuator with Electrodes Exhibiting Asymmetric Resistance

    PubMed Central

    Feng, Guo-Hua; Liu, Kim-Min

    2014-01-01

    This paper presents a swirl-shaped microfeatured ionic polymer-metal composite (IPMC) actuator. A novel micromachining process was developed to fabricate an array of IPMC actuators on a glass substrate and to ensure that no shortcircuits occur between the electrodes of the actuator. We demonstrated a microfluidic scheme in which surface tension was used to construct swirl-shaped planar IPMC devices of microfeature size and investigated the flow velocity of Nafion solutions, which formed the backbone polymer of the actuator, within the microchannel. The unique fabrication process yielded top and bottom electrodes that exhibited asymmetric surface resistance. A tool for measuring surface resistance was developed and used to characterize the resistances of the electrodes for the fabricated IPMC device. The actuator, which featured asymmetric electrode resistance, caused a nonzero-bias current when the device was driven using a zero-bias square wave, and we propose a circuit model to describe this phenomenon. Moreover, we discovered and characterized a bending and rotating motion when the IPMC actuator was driven using a square wave. We observed a strain rate of 14.6% and a displacement of 700 μm in the direction perpendicular to the electrode surfaces during 4.5-V actuation. PMID:24824370

  9. Fabrication and characterization of a micromachined swirl-shaped ionic polymer metal composite actuator with electrodes exhibiting asymmetric resistance.

    PubMed

    Feng, Guo-Hua; Liu, Kim-Min

    2014-05-12

    This paper presents a swirl-shaped microfeatured ionic polymer-metal composite (IPMC) actuator. A novel micromachining process was developed to fabricate an array of IPMC actuators on a glass substrate and to ensure that no shortcircuits occur between the electrodes of the actuator. We demonstrated a microfluidic scheme in which surface tension was used to construct swirl-shaped planar IPMC devices of microfeature size and investigated the flow velocity of Nafion solutions, which formed the backbone polymer of the actuator, within the microchannel. The unique fabrication process yielded top and bottom electrodes that exhibited asymmetric surface resistance. A tool for measuring surface resistance was developed and used to characterize the resistances of the electrodes for the fabricated IPMC device. The actuator, which featured asymmetric electrode resistance, caused a nonzero-bias current when the device was driven using a zero-bias square wave, and we propose a circuit model to describe this phenomenon. Moreover, we discovered and characterized a bending and rotating motion when the IPMC actuator was driven using a square wave. We observed a strain rate of 14.6% and a displacement of 700 μm in the direction perpendicular to the electrode surfaces during 4.5-V actuation.

  10. Effect of fabrication-dependent shape and composition of solid-state nanopores on single nanoparticle detection.

    PubMed

    Liu, Shuo; Yuzvinsky, Thomas D; Schmidt, Holger

    2013-06-25

    Solid-state nanopores can be fabricated in a variety of ways and form the basis for label-free sensing of single nanoparticles: as individual nanoparticles traverse the nanopore, they alter the ionic current across it in a characteristic way. Typically, nanopores are described by the diameter of their limiting aperture, and less attention has been paid to other, fabrication-dependent parameters. Here, we report a comprehensive analysis of the properties and sensing performance of three types of nanopore with identical 50 nm aperture, but fabricated using three different techniques: direct ion beam milling, ion beam sculpting, and electron beam sculpting. The nanopores differ substantially in physical shape and chemical composition as identified by ion-beam assisted cross-sectioning and energy dispersive X-ray spectroscopy. Concomitant differences in electrical sensing of single 30 nm beads, such as variations in blockade depth, duration, and electric field dependence, are observed and modeled using hydrodynamic simulations. The excellent agreement between experiment and physical modeling shows that the physical properties (shape) and not the chemical surface composition determine the sensing performance of a solid-state nanopore in the absence of deliberate surface modification. Consequently, nanoparticle sensing performance can be accurately predicted once the full three-dimensional structure of the nanopore is known.

  11. Inhibitor Discovery by Convolution ABPP.

    PubMed

    Chandrasekar, Balakumaran; Hong, Tram Ngoc; van der Hoorn, Renier A L

    2017-01-01

    Activity-based protein profiling (ABPP) has emerged as a powerful proteomic approach to study the active proteins in their native environment by using chemical probes that label active site residues in proteins. Traditionally, ABPP is classified as either comparative or competitive ABPP. In this protocol, we describe a simple method called convolution ABPP, which takes benefit from both the competitive and comparative ABPP. Convolution ABPP allows one to detect if a reduced signal observed during comparative ABPP could be due to the presence of inhibitors. In convolution ABPP, the proteomes are analyzed by comparing labeling intensities in two mixed proteomes that were labeled either before or after mixing. A reduction of labeling in the mix-and-label sample when compared to the label-and-mix sample indicates the presence of an inhibitor excess in one of the proteomes. This method is broadly applicable to detect inhibitors in proteomes against any proteome containing protein activities of interest. As a proof of concept, we applied convolution ABPP to analyze secreted proteomes from Pseudomonas syringae-infected Nicotiana benthamiana leaves to display the presence of a beta-galactosidase inhibitor.

  12. Fabrication and characterization of TiO2 coated cone shaped nano-fiber pH sensor

    NASA Astrophysics Data System (ADS)

    Pathak, A. K.; Bhardwaj, V.; Gangwar, R. K.; De, M.; Singh, V. K.

    2017-03-01

    In the present paper a novel cone shaped nano-fiber (CSNF) pH sensor using multi-mode fiber (MMF) has been fabricated and demonstrated. Three different pH indicators, chlorophenol red, bromothymol blue and cresol red with precursor tetraethyl orthosilicate (TEOS) have been used for fabrication of pH sensing layer. A significant enhancement in sensing properties of pH sensor with TiO2 thin film has been observed. The pH sensor with TiO2 thin film shows the quite high sensitivity (1.16 dBm/pH) as compared to sensor with simple pH coating (0.81 dBm/pH) at 1550 nm with a good linear response. Moreover, the sensor with TiO2 film exhibits fast response time of ∼ 25 s for pH values ranging from 4 to 11 with excellent stability and durability.

  13. Fabrication of a pen-shaped portable biochemical reaction system based on magnetic bead manipulation

    NASA Astrophysics Data System (ADS)

    Shikida, Mitsuhiro; Inagaki, Noriyuki; Okochi, Mina; Honda, Hiroyuki; Sato, Kazuo

    2011-06-01

    A pen-shaped platform that is similar to a mechanical pencil is proposed for producing a portable reaction system. A reaction unit, as the key component in the system, was produced by using a heat shrinkable tube. A mechanical pencil supplied by Mitsubishi Pencil Co. Ltd was used as the pen-shaped platform for driving the reaction cylinder. It was actuated using an inchworm motion. We confirmed that the magnetic beads were successfully manipulated in the droplet in the cylinder-shaped reaction units.

  14. Methods of fabricating a conductor assembly having a curvilinear arcuate shape

    DOEpatents

    Meinke, Rainer

    2011-08-23

    A method for manufacture of a conductor assembly along a curvilinear axis. The assembly may be of the type which, when conducting current, generates a magnetic field or in which, in the presence of a changing magnetic field, a voltage is induced. In one example, the assembly includes a structure having a curved shape extending along the axis. A surface of the structure is positioned for formation of a channel along the curved shape. The structure is rotated about a second axis. While rotating the structure, a channel is formed in the surface that results in a helical shape in the structure. The channel extends both around and along the first axis.

  15. Fabrication and static characterization of carbon-fiber-reinforced polymers with embedded NiTi shape memory wire actuators

    NASA Astrophysics Data System (ADS)

    de Araújo, C. J.; Rodrigues, L. F. A.; Coutinho Neto, J. F.; Reis, R. P. B.

    2008-12-01

    In this work, unidirectional carbon-fiber-reinforced polymers (CFRP) with embedded NiTi shape memory alloy (SMA) wire actuators were manufactured using a universal testing machine equipped with a thermally controlled chamber. Beam specimens containing cold-worked, annealed and trained NiTi SMA wires distributed along their neutral plane were fabricated. Several tests in a three-point bending mode at different constant temperatures were performed. To verify thermal buckling effects, electrical activation of the specimens was realized in a cantilevered beam mode and the influence of the SMA wire actuators on the tip deflection of the composite is demonstrated.

  16. Fabrication of Ion-Shaped Anisotropic Nanoparticles and their Orientational Imaging by Second-Harmonic Generation Microscopy

    PubMed Central

    Slablab, Abdallah; Isotalo, Tero J.; Mäkitalo, Jouni; Turquet, Léo; Coulon, Pierre-Eugène; Niemi, Tapio; Ulysse, Christian; Kociak, Mathieu; Mailly, Dominique; Rizza, Giancarlo; Kauranen, Martti

    2016-01-01

    Ion beam shaping is a novel and powerful tool to engineer nanocomposites with effective three-dimensional (3D) architectures. In particular, this technique offers the possibility to precisely control the size, shape and 3D orientation of metallic nanoparticles at the nanometer scale while keeping the particle volume constant. Here, we use swift heavy ions of xenon for irradiation in order to successfully fabricate nanocomposites consisting of anisotropic gold nanoparticle that are oriented in 3D and embedded in silica matrix. Furthermore, we investigate individual nanorods using a nonlinear optical microscope based on second-harmonic generation (SHG). A tightly focused linearly or radially-polarized laser beam is used to excite nanorods with different orientations. We demonstrate high sensitivity of the SHG response for these polarizations to the orientation of the nanorods. The SHG measurements are in excellent agreement with the results of numerical modeling based on the boundary element method. PMID:27881838

  17. Fabrication of Ion-Shaped Anisotropic Nanoparticles and their Orientational Imaging by Second-Harmonic Generation Microscopy

    NASA Astrophysics Data System (ADS)

    Slablab, Abdallah; Isotalo, Tero J.; Mäkitalo, Jouni; Turquet, Léo; Coulon, Pierre-Eugène; Niemi, Tapio; Ulysse, Christian; Kociak, Mathieu; Mailly, Dominique; Rizza, Giancarlo; Kauranen, Martti

    2016-11-01

    Ion beam shaping is a novel and powerful tool to engineer nanocomposites with effective three-dimensional (3D) architectures. In particular, this technique offers the possibility to precisely control the size, shape and 3D orientation of metallic nanoparticles at the nanometer scale while keeping the particle volume constant. Here, we use swift heavy ions of xenon for irradiation in order to successfully fabricate nanocomposites consisting of anisotropic gold nanoparticle that are oriented in 3D and embedded in silica matrix. Furthermore, we investigate individual nanorods using a nonlinear optical microscope based on second-harmonic generation (SHG). A tightly focused linearly or radially-polarized laser beam is used to excite nanorods with different orientations. We demonstrate high sensitivity of the SHG response for these polarizations to the orientation of the nanorods. The SHG measurements are in excellent agreement with the results of numerical modeling based on the boundary element method.

  18. Direct Fabrication of Free-Standing MOF Superstructures with Desired Shapes by Micro-Confined Interfacial Synthesis.

    PubMed

    Kim, Jin-Oh; Min, Kyoung-Ik; Noh, Hyunwoo; Kim, Dong-Hwi; Park, Soo-Young; Kim, Dong-Pyo

    2016-06-13

    Recently, metal-organic frameworks (MOFs) with multifunctional pore chemistry have been intensively investigated for positioning the desired morphology at specific locations onto substrates for manufacturing devices. Herein, we develop a micro-confined interfacial synthesis (MIS) approach for fabrication of a variety of free-standing MOF superstructures with desired shapes. This approach for engineering MOFs provides three key features: 1) in situ synthesis of various free-standing MOF superstructures with controlled compositions, shape, and thickness using a mold membrane; 2) adding magnetic functionality into MOF superstructures by loading with Fe3 O4 nanoparticles; 3) transferring the synthesized MOF superstructural array on to flat or curved surface of various substrates. The MIS route with versatile potential opens the door for a number of new perspectives in various applications.

  19. Convolutional neural network for pottery retrieval

    NASA Astrophysics Data System (ADS)

    Benhabiles, Halim; Tabia, Hedi

    2017-01-01

    The effectiveness of the convolutional neural network (CNN) has already been demonstrated in many challenging tasks of computer vision, such as image retrieval, action recognition, and object classification. This paper specifically exploits CNN to design local descriptors for content-based retrieval of complete or nearly complete three-dimensional (3-D) vessel replicas. Based on vector quantization, the designed descriptors are clustered to form a shape vocabulary. Then, each 3-D object is associated to a set of clusters (words) in that vocabulary. Finally, a weighted vector counting the occurrences of every word is computed. The reported experimental results on the 3-D pottery benchmark show the superior performance of the proposed method.

  20. Fabrication and characterization of shape memory polymers at small-scales

    NASA Astrophysics Data System (ADS)

    Wornyo, Edem

    The objective of this research is to thoroughly investigate the shape memory effect in polymers, characterize, and optimize these polymers for applications in information storage systems. Previous research effort in this field concentrated on shape memory metals for biomedical applications such as stents. Minimal work has been done on shape memory polymers; and the available work on shape memory polymers has not characterized the behaviors of this category of polymers fully. Copolymer shape memory materials based on diethylene glycol dimethacrylate (DEGDMA) crosslinker, and tert butyl acrylate (tBA) monomer are designed. The design encompasses a careful control of the backbone chemistry of the materials. Characterization methods such as dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC); and novel nanoscale techniques such as atomic force microscopy (AFM), and nanoindentation are applied to this system of materials. Designed experiments are conducted on the materials to optimize spin coating conditions for thin films. Furthermore, the recovery, a key for the use of these polymeric materials for information storage, is examined in detail with respect to temperature. In sum, the overarching objectives of the proposed research are to: (i) Design shape memory polymers based on polyethylene glycol dimethacrylate (PEGDMA) and diethylene glycol dimethacrylate (DEGDMA) crosslinkers, 2-hydroxyethyl methacrylate (HEMA) and tert-butyl acrylate monomer (tBA). (ii) Utilize dynamic mechanical analysis (DMA) to comprehend the thermomechanical properties of shape memory polymers based on DEGDMA and tBA. (iii) Utilize nanoindentation and atomic force microscopy (AFM) to understand the nanoscale behavior of these SMPs, and explore the strain storage and recovery of the polymers from a deformed state. (iv) Study spin coating conditions on thin film quality with designed experiments. (iv) Apply neural networks and genetic algorithms to optimize these systems.

  1. Prototype fabrication and preliminary in vitro testing of a shape memory endovascular thrombectomy device.

    PubMed

    Small, Ward; Wilson, Thomas S; Buckley, Patrick R; Benett, William J; Loge, Jeffrey M; Hartman, Jonathan; Maitland, Duncan J

    2007-09-01

    An electromechanical microactuator comprised of shape memory polymer (SMP) and shape memory nickel-titanium alloy (nitinol) was developed and used in an endovascular thrombectomy device prototype. The microactuator maintains a straight rod shape until an applied current induces electro-resistive (Joule) heating, causing the microactuator to transform into a corkscrew shape. The straight-to-corkscrew transformation geometry was chosen to permit endovascular delivery through (straight form) and retrieval of (corkscrew form) a stroke-causing thrombus (blood clot) in the brain. Thermal imaging of the microactuator during actuation in air indicated that the steady-state temperature rise caused by Joule heating varied quadratically with applied current and that actuation occurred near the glass transition temperature of the SMP (86 degrees C). To demonstrate clinical application, the device was used to retrieve a blood clot in a water-filled silicone neurovascular model. Numerical modeling of the heat transfer to the surrounding blood and associated thermal effects on the adjacent artery potentially encountered during clinical use suggested that any thermal damage would likely be confined to localized areas where the microactuator was touching the artery wall. This shape memory mechanical thrombectomy device is a promising tool for treating ischemic stroke without the need for infusion of clot-dissolving drugs.

  2. Hydrothermal fabrication of octahedral-shaped Fe3O4 nanoparticles and their magnetorheological response

    NASA Astrophysics Data System (ADS)

    Jung, H. S.; Choi, H. J.

    2015-05-01

    Octahedral-shaped Fe3O4 nanoparticles were synthesized in the presence of 1,3-diaminopropane using a hydrothermal method and assessed as a potential magnetorheological (MR) material. Their morphology, crystal structure, and magnetic properties were examined by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and vibrating sample magnetometry, respectively. The MR characteristics of the octahedral-shaped, Fe3O4 nanoparticle-based MR particles when dispersed in silicone oil with a 10 vol. % particle concentration were examined using a rotational rheometer under an external magnetic field. The resulting MR fluids exhibited a Bingham-like behavior with a distinctive yield stress from their flow curves.

  3. Properties of Porous TiNbZr Shape Memory Alloy Fabricated by Mechanical Alloying and Hot Isostatic Pressing

    NASA Astrophysics Data System (ADS)

    Ma, L. W.; Chung, C. Y.; Tong, Y. X.; Zheng, Y. F.

    2011-07-01

    In the past decades, systematic researches have been focused on studying Ti-Nb-based SMAs by adding ternary elements, such as Mo, Sn, Zr, etc. However, only arc melting or induction melting methods, with subsequent hot or cold rolling, were used to fabricate these Ni-free SMAs. There is no work related to powder metallurgy and porous structures. This study focuses on the fabrication and characterization of porous Ti-22Nb-6Zr (at.%) shape memory alloys produced using elemental powders by means of mechanical alloying and hot isostatic pressing. It is found that the porous Ti-22Nb-6Zr alloys prepared by the HIP process exhibit a homogenous pore distribution with spherical pores, while the pores have irregular shape in the specimen prepared by conventional sintering. X-ray diffraction analysis showed that the solid solution-treated Ti-22Nb-6Zr alloy consists of both β phase and α″ martensite phase. Morphologies of martensite were observed. Finally, the porous Ti-22Nb-6Zr SMAs produced by both MA and HIP exhibit good mechanical properties, such as superior superelasticity, with maximum recoverable strain of ~3% and high compressive strength.

  4. Influence of cell shape on mechanical properties of Ti-6Al-4V meshes fabricated by electron beam melting method.

    PubMed

    Li, S J; Xu, Q S; Wang, Z; Hou, W T; Hao, Y L; Yang, R; Murr, L E

    2014-10-01

    Ti-6Al-4V reticulated meshes with different elements (cubic, G7 and rhombic dodecahedron) in Materialise software were fabricated by additive manufacturing using the electron beam melting (EBM) method, and the effects of cell shape on the mechanical properties of these samples were studied. The results showed that these cellular structures with porosities of 88-58% had compressive strength and elastic modulus in the range 10-300MPa and 0.5-15GPa, respectively. The compressive strength and deformation behavior of these meshes were determined by the coupling of the buckling and bending deformation of struts. Meshes that were dominated by buckling deformation showed relatively high collapse strength and were prone to exhibit brittle characteristics in their stress-strain curves. For meshes dominated by bending deformation, the elastic deformation corresponded well to the Gibson-Ashby model. By enhancing the effect of bending deformation, the stress-strain curve characteristics can change from brittle to ductile (the smooth plateau area). Therefore, Ti-6Al-4V cellular solids with high strength, low modulus and desirable deformation behavior could be fabricated through the cell shape design using the EBM technique.

  5. A study of shape-dependent partial volume correction in pet imaging using ellipsoidal phantoms fabricated via rapid prototyping

    NASA Astrophysics Data System (ADS)

    Mille, Matthew M.

    Positron emission tomography (PET) with 2-[18F]fluoro-2-deoxy-D-glucose (FDG) is being increasingly recognized as an important tool for quantitative assessment of tumor response because of its ability to capture functional information about the tumor's metabolism. However, despite many advances in PET technology, measurements of tumor radiopharmaceutical uptake in PET are still challenged by issues of accuracy and consistency, thereby compromising the use of PET as a surrogate endpoint in clinical trials. One limiting component of the overall uncertainty in PET is the relatively poor spatial resolution of the images which directly affects the accuracy of the tumor radioactivity measurements. These spatial resolution effects, colloquially known as the partial volume effect (PVE), are a function of the characteristics of the scanner as well as the tumor being imaged. Previous efforts have shown that the PVE depends strongly on the tumor volume and the background-to-tumor activity concentration ratio. The PVE is also suspected to be a function of tumor shape, although to date no systematic study of this effect has been performed. This dissertation seeks to help fill the gap in the current knowledge about the shape-dependence of the PVE by attempting to quantify, through both theoretical calculation and experimental measurement, the magnitude of the shape effect for ellipsoidal tumors. An experimental investigation of the tumor shape effect necessarily requires tumor phantoms of multiple shapes. Hence, a prerequisite for this research was the design and fabrication of hollow tumor phantoms which could be filled uniformly with radioactivity and imaged on a PET scanner. The phantom fabrication was achieved with the aid of stereolithography and included prolate ellipsoids of various axis ratios. The primary experimental method involved filling the tumor phantoms with solutions of 18F whose activity concentrations were known and traceable to primary radioactivity standards

  6. Roll-to-roll hot embossing system with shape preserving mechanism for the large-area fabrication of microstructures

    NASA Astrophysics Data System (ADS)

    Peng, Linfa; Wu, Hao; Shu, Yunyi; Yi, Peiyun; Deng, Yujun; Lai, Xinmin

    2016-10-01

    Roll-to-roll (R2R) hot embossing is a promising approach to fulfilling the demands of high throughput fabrication of large-area polymeric components with micro-structure arrays which have been widely employed in the domains of optics, optoelectronics, biology, chemistry, etc. Nevertheless, the characteristic of continuous and fast forming for the R2R hot embossing process limits material flow during filling stage and results in significant springback during demolding stage. As a result, forming defects usually appear and the process window is very narrow which hinders the industrialization of this technology. This study developed a R2R hot embossing machine and proposed a shape preserving mechanism to extend the material filling time and realized the cooling effect during the demolding process. Comparative experiments were conducted on the R2R hot embossing process for micro-pyramid arrays to understand the effect of shape preserving mechanism. The influence of tension force and encapsulation angle to the forming quality was systematically analyzed. Furthermore, the influence of processing parameters has been investigated by using the one-variable-at-a-time method. Afterwards, a series of experiments based on the central composite design approach have been conducted for the analysis of variance and the establishment of empirical models of the R2R hot embossing process. As a result, the process window was extended by the shape preserving mechanism. More importantly, the feeding speed was improved from 0.5 m min-1 to 2.5 m min-1 for the large-area fabrication of micro-pyramid arrays, which is very attractive to the industrialization of this technology.

  7. Nanograined Net-Shaped Fabrication of Rhenium Components by EB-PVD

    SciTech Connect

    Singh, Jogender; Wolfe, Douglas E.

    2004-02-04

    Cost-effective net-shaped forming components have brought considerable interest into DoD, NASA and DoE. Electron beam physical vapor deposition (EB-PVD) offers flexibility in forming net-shaped components with tailored microstructure and chemistry. High purity rhenium (Re) components including rhenium-coated graphite balls, Re- plates and tubes have been successfully manufactured by EB-PVD. EB-PVD Re components exhibited sub-micron and nano-sized grains with high hardness and strength as compared to CVD. It is estimated that the cost of Re components manufactured by EB-PVD would be less than the current CVD and powder-HIP Technologies.

  8. Electron Beam Freeform Fabrication (EBF3) for Cost Effective Near-Net Shape Manufacturing

    NASA Technical Reports Server (NTRS)

    Taminger, Karen M.; Hafley, Robert A.

    2006-01-01

    Manufacturing of structural metal parts directly from computer aided design (CAD) data has been investigated by numerous researchers over the past decade. Researchers at NASA Langley Research Center are developing a new solid freeform fabrication process, electron beam freeform fabrication (EBF3), as a rapid metal deposition process that works efficiently with a variety of weldable alloys. EBF3 deposits of 2219 aluminium and Ti-6Al-4V have exhibited a range of grain morphologies depending upon the deposition parameters. These materials have exhibited excellent tensile properties comparable to typical handbook data for wrought plate product after post-processing heat treatments. The EBF3 process is capable of bulk metal deposition at deposition rates in excess of 2500 cubic centimeters per hour (150 in3/hr) or finer detail at lower deposition rates, depending upon the desired application. This process offers the potential for rapidly adding structural details to simpler cast or forged structures rather than the conventional approach of machining large volumes of chips to produce a monolithic metallic structure. Selective addition of metal onto simpler blanks of material can have a significant effect on lead time reduction and lower material and machining costs.

  9. Electron Beam Freeform Fabrication for Cost Effective Near-Net Shape Manufacturing

    NASA Technical Reports Server (NTRS)

    Taminger, Karen M.; Hafley, Robert A.

    2006-01-01

    Manufacturing of structural metal parts directly from computer aided design (CAD) data has been investigated by numerous researchers over the past decade. Researchers at NASA Langley Research Center are developing a new solid freeform fabrication process, electron beam freeform fabrication (EBF3), as a rapid metal deposition process that works efficiently with a variety of weldable alloys. EBF3 deposits of 2219 aluminium and Ti-6Al-4V have exhibited a range of grain morphologies depending upon the deposition parameters. These materials have exhibited excellent tensile properties comparable to typical handbook data for wrought plate product after post-processing heat treatments. The EBF3 process is capable of bulk metal deposition at deposition rates in excess of 2500 cm3/hr (150 in3/hr) or finer detail at lower deposition rates, depending upon the desired application. This process offers the potential for rapidly adding structural details to simpler cast or forged structures rather than the conventional approach of machining large volumes of chips to produce a monolithic metallic structure. Selective addition of metal onto simpler blanks of material can have a significant effect on lead time reduction and lower material and machining costs.

  10. Top-Down Particle Fabrication: Control of Size and Shape for Diagnostic Imaging and Drug Delivery

    PubMed Central

    Canelas, Dorian A.; Herlihy, Kevin P.; DeSimone, Joseph M.

    2009-01-01

    This review discusses rational design of particles for use as therapeutic vectors and diagnostic imaging agent carriers. The emerging importance of both particle size and shape is considered, and the adaptation and modification of soft lithography methods to produce nanoparticles is highlighted. To this end, studies utilizing particles made via a process called Particle Replication In Non-wetting Templates (PRINT™) are discussed. In addition, insights gained into therapeutic cargo and imaging agent delivery from related types of polymer-based carriers are considered. PMID:20049805

  11. Texture and Crystal Orientation in Ti-6Al-4V Builds Fabricated by Shaped Metal Deposition

    NASA Astrophysics Data System (ADS)

    Baufeld, Bernd; van der Biest, Omer; Dillien, Steven

    2010-08-01

    The texture and crystal orientation of Ti-6Al-4V components, manufactured by shaped metal deposition (SMD), is investigated. SMD is a novel rapid prototyping tungsten inert gas (TIG) welding technique leading to near-net-shape components. This involves sequential layer by layer deposition with repeated partial melting and heat treatment, which results in epitaxial growth of large elongated prior β grains. This leads to a directionally solidified texture, where the prior β grains exhibit only a small misorientation with each other. The β grains grow in left< { 100} rightrangle direction with a second left< { 100} rightrangle direction perpendicular to the wall surface. During cooling, the α phase transformation follows the Burgers orientation relationship leading to a Widmanstätten structure, with orientation relations between most of the α lamellae and also of the residual β phase. The directionally solidification and the transformation into the α phase following the Burgers relationship results in a texture, where the hcp pole figures look similar to bcc pole figures.

  12. Cantilever tilt causing amplitude related convolution in dynamic mode atomic force microscopy.

    PubMed

    Wang, Chunmei; Sun, Jielin; Itoh, Hiroshi; Shen, Dianhong; Hu, Jun

    2011-01-01

    It is well known that the topography in atomic force microscopy (AFM) is a convolution of the tip's shape and the sample's geometry. The classical convolution model was established in contact mode assuming a static probe, but it is no longer valid in dynamic mode AFM. It is still not well understood whether or how the vibration of the probe in dynamic mode affects the convolution. Such ignorance complicates the interpretation of the topography. Here we propose a convolution model for dynamic mode by taking into account the typical design of the cantilever tilt in AFMs, which leads to a different convolution from that in contact mode. Our model indicates that the cantilever tilt results in a dynamic convolution affected by the absolute value of the amplitude, especially in the case that corresponding contact convolution has sharp edges beyond certain angle. The effect was experimentally demonstrated by a perpendicular SiO(2)/Si super-lattice structure. Our model is useful for quantitative characterizations in dynamic mode, especially in probe characterization and critical dimension measurements.

  13. Highly stretchable and shape-controllable three-dimensional antenna fabricated by “Cut-Transfer-Release” method

    PubMed Central

    Yan, Zhuocheng; Pan, Taisong; Yao, Guang; Liao, Feiyi; Huang, Zhenlong; Zhang, Hulin; Gao, Min; Zhang, Yin; Lin, Yuan

    2017-01-01

    Recent progresses on the Kirigami-inspired method provide a new idea to assemble three-dimensional (3D) functional structures with conventional materials by releasing the prestrained elastomeric substrates. In this paper, highly stretchable serpentine-like antenna is fabricated by a simple and quick “Cut-Transfer-Release” method for assembling stretchable 3D functional structures on an elastomeric substrate with a controlled shape. The mechanical reliability of the serpentine-like 3D stretchable antenna is evaluated by the finite element method and experiments. The antenna shows consistent radio frequency performance with center frequency at 5.6 GHz during stretching up to 200%. The 3D structure is also able to eliminate the hand effect observed commonly in the conventional antenna. This work is expected to spur the applications of novel 3D structures in the stretchable electronics. PMID:28198812

  14. Highly stretchable and shape-controllable three-dimensional antenna fabricated by "Cut-Transfer-Release" method.

    PubMed

    Yan, Zhuocheng; Pan, Taisong; Yao, Guang; Liao, Feiyi; Huang, Zhenlong; Zhang, Hulin; Gao, Min; Zhang, Yin; Lin, Yuan

    2017-02-13

    Recent progresses on the Kirigami-inspired method provide a new idea to assemble three-dimensional (3D) functional structures with conventional materials by releasing the prestrained elastomeric substrates. In this paper, highly stretchable serpentine-like antenna is fabricated by a simple and quick "Cut-Transfer-Release" method for assembling stretchable 3D functional structures on an elastomeric substrate with a controlled shape. The mechanical reliability of the serpentine-like 3D stretchable antenna is evaluated by the finite element method and experiments. The antenna shows consistent radio frequency performance with center frequency at 5.6 GHz during stretching up to 200%. The 3D structure is also able to eliminate the hand effect observed commonly in the conventional antenna. This work is expected to spur the applications of novel 3D structures in the stretchable electronics.

  15. Highly stretchable and shape-controllable three-dimensional antenna fabricated by “Cut-Transfer-Release” method

    NASA Astrophysics Data System (ADS)

    Yan, Zhuocheng; Pan, Taisong; Yao, Guang; Liao, Feiyi; Huang, Zhenlong; Zhang, Hulin; Gao, Min; Zhang, Yin; Lin, Yuan

    2017-02-01

    Recent progresses on the Kirigami-inspired method provide a new idea to assemble three-dimensional (3D) functional structures with conventional materials by releasing the prestrained elastomeric substrates. In this paper, highly stretchable serpentine-like antenna is fabricated by a simple and quick “Cut-Transfer-Release” method for assembling stretchable 3D functional structures on an elastomeric substrate with a controlled shape. The mechanical reliability of the serpentine-like 3D stretchable antenna is evaluated by the finite element method and experiments. The antenna shows consistent radio frequency performance with center frequency at 5.6 GHz during stretching up to 200%. The 3D structure is also able to eliminate the hand effect observed commonly in the conventional antenna. This work is expected to spur the applications of novel 3D structures in the stretchable electronics.

  16. Simplified Decoding of Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Truong, T. K.; Reed, I. S.

    1986-01-01

    Some complicated intermediate steps shortened or eliminated. Decoding of convolutional error-correcting digital codes simplified by new errortrellis syndrome technique. In new technique, syndrome vector not computed. Instead, advantage taken of newly-derived mathematical identities simplify decision tree, folding it back on itself into form called "error trellis." This trellis graph of all path solutions of syndrome equations. Each path through trellis corresponds to specific set of decisions as to received digits. Existing decoding algorithms combined with new mathematical identities reduce number of combinations of errors considered and enable computation of correction vector directly from data and check bits as received.

  17. Net Shaped Component Fabrication of Refractory Metal Alloys using Vacuum Plasma Spraying

    NASA Technical Reports Server (NTRS)

    Sen, S.; ODell, S.; Gorti, S.; Litchford, R.

    2006-01-01

    The vacuum plasma spraying (VPS) technique was employed to produce dense and net shaped components of a new tungsten-rhenium (W-Re) refractory metal alloy. The fine grain size obtained using this technique enhanced the mechanical properties of the alloy at elevated temperatures. The alloy development also included incorporation of thermodynamically stable dispersion phases to pin down grain boundaries at elevated temperatures and thereby circumventing the inherent problem of recrystallization of refractory alloys at elevated temperatures. Requirements for such alloys as related to high temperature space propulsion components will be discussed. Grain size distribution as a function of cooling rate and dispersion phase loading will be presented. Mechanical testing and grain growth results as a function of temperature will also be discussed.

  18. On the growth and form of cortical convolutions

    NASA Astrophysics Data System (ADS)

    Tallinen, Tuomas; Chung, Jun Young; Rousseau, François; Girard, Nadine; Lefèvre, Julien; Mahadevan, L.

    2016-06-01

    The rapid growth of the human cortex during development is accompanied by the folding of the brain into a highly convoluted structure. Recent studies have focused on the genetic and cellular regulation of cortical growth, but understanding the formation of the gyral and sulcal convolutions also requires consideration of the geometry and physical shaping of the growing brain. To study this, we use magnetic resonance images to build a 3D-printed layered gel mimic of the developing smooth fetal brain; when immersed in a solvent, the outer layer swells relative to the core, mimicking cortical growth. This relative growth puts the outer layer into mechanical compression and leads to sulci and gyri similar to those in fetal brains. Starting with the same initial geometry, we also build numerical simulations of the brain modelled as a soft tissue with a growing cortex, and show that this also produces the characteristic patterns of convolutions over a realistic developmental course. All together, our results show that although many molecular determinants control the tangential expansion of the cortex, the size, shape, placement and orientation of the folds arise through iterations and variations of an elementary mechanical instability modulated by early fetal brain geometry.

  19. The trellis complexity of convolutional codes

    NASA Technical Reports Server (NTRS)

    Mceliece, R. J.; Lin, W.

    1995-01-01

    It has long been known that convolutional codes have a natural, regular trellis structure that facilitates the implementation of Viterbi's algorithm. It has gradually become apparent that linear block codes also have a natural, though not in general a regular, 'minimal' trellis structure, which allows them to be decoded with a Viterbi-like algorithm. In both cases, the complexity of the Viterbi decoding algorithm can be accurately estimated by the number of trellis edges per encoded bit. It would, therefore, appear that we are in a good position to make a fair comparison of the Viterbi decoding complexity of block and convolutional codes. Unfortunately, however, this comparison is somewhat muddled by the fact that some convolutional codes, the punctured convolutional codes, are known to have trellis representations that are significantly less complex than the conventional trellis. In other words, the conventional trellis representation for a convolutional code may not be the minimal trellis representation. Thus, ironically, at present we seem to know more about the minimal trellis representation for block than for convolutional codes. In this article, we provide a remedy, by developing a theory of minimal trellises for convolutional codes. (A similar theory has recently been given by Sidorenko and Zyablov). This allows us to make a direct performance-complexity comparison for block and convolutional codes. A by-product of our work is an algorithm for choosing, from among all generator matrices for a given convolutional code, what we call a trellis-minimal generator matrix, from which the minimal trellis for the code can be directly constructed. Another by-product is that, in the new theory, punctured convolutional codes no longer appear as a special class, but simply as high-rate convolutional codes whose trellis complexity is unexpectedly small.

  20. Design and fabrication of a bat-inspired flapping-flight platform using shape memory alloy muscles and joints

    NASA Astrophysics Data System (ADS)

    Furst, Stephen J.; Bunget, George; Seelecke, Stefan

    2013-01-01

    This work focuses on the development of a concept for a micro-air vehicle (MAV) based on a bio-inspired flapping motion that is generated from integrated smart materials. Since many smart materials have their own biomimetic characteristics and the potential to be highly efficient, lightweight, and streamlined, they are ideal candidates for use in structural or actuator components in MAVs. In this work, shape memory alloy (SMA) actuator wires are used as analogs for biological muscles, and super-elastic SMAs are implemented as flexible joints capable of large bending angles. While biological organisms have an intrinsic sensing array composed of nerves, the SMA wires also provide self-sensing by virtue of a phase-dependent resistance change. Study of the biology and flight characteristics of natural fliers concluded that the bat provides an ideal platform for SMA muscle wires because of its comparatively low wingbeat frequency and superb maneuverability. A first-generation prototype is built to further the understanding of fabricating Nature’s designs. The engineering design is then improved further in a second-generation prototype that combines 3D printing and new techniques for embedding SMA wires and shaping SMA joints for improved robustness, reproducibility, and lifetime. These prototypes are on display at the North Carolina Museum of Natural Science’s Nature Research Center, which has the goal of bridging the gaps between biology and engineering.

  1. Convolution-deconvolution in DIGES

    SciTech Connect

    Philippacopoulos, A.J.; Simos, N.

    1995-05-01

    Convolution and deconvolution operations is by all means a very important aspect of SSI analysis since it influences the input to the seismic analysis. This paper documents some of the convolution/deconvolution procedures which have been implemented into the DIGES code. The 1-D propagation of shear and dilatational waves in typical layered configurations involving a stack of layers overlying a rock is treated by DIGES in a similar fashion to that of available codes, e.g. CARES, SHAKE. For certain configurations, however, there is no need to perform such analyses since the corresponding solutions can be obtained in analytic form. Typical cases involve deposits which can be modeled by a uniform halfspace or simple layered halfspaces. For such cases DIGES uses closed-form solutions. These solutions are given for one as well as two dimensional deconvolution. The type of waves considered include P, SV and SH waves. The non-vertical incidence is given special attention since deconvolution can be defined differently depending on the problem of interest. For all wave cases considered, corresponding transfer functions are presented in closed-form. Transient solutions are obtained in the frequency domain. Finally, a variety of forms are considered for representing the free field motion both in terms of deterministic as well as probabilistic representations. These include (a) acceleration time histories, (b) response spectra (c) Fourier spectra and (d) cross-spectral densities.

  2. Easy and cheap fabrication of ordered pyramidal-shaped plasmonic substrates for detection and quantitative analysis using surface-enhanced Raman spectroscopy.

    PubMed

    Leordean, Cosmin; Gabudean, Ana-Maria; Canpean, Valentin; Astilean, Simion

    2013-09-07

    In this work we present a simple approach for the fabrication of periodically ordered pyramidal-shaped metallic nanostructures and demonstrate their efficiency as SERS active substrates. Our method for the fabrication of the plasmonic substrate is based on nanoimprint lithography and exploits the thermal properties of two classes of polymers, thermoplastics and hydrogels. During the heating process the thermoplastic polymers will start to melt whereas the hydrogel polymers will form a solid due to the evaporation of water molecules adsorbed during the dissolving process. Using this approach we fabricate highly ordered pyramidal-shaped nanostructures using the texture of a commercial DVD as the initial mold. This technique represents a low-cost alternative to the classical lithography techniques, allowing the fabrication over large areas (~cm(2)) of periodically ordered nanostructures in a controlled and reproducible manner. The SERS efficiency of the fabricated substrate is demonstrated through the detection of urea molecules found in the fingerprint. In addition, due to the periodicity of the pyramidal-shaped structures, the fabricated substrate can be successfully employed to correlate the intensity of the specific SERS peak of urea with the molecules concentration, offering thus the possibility of developing a quantitative SERS renal sensor.

  3. Shape-controlled fabrication of magnetite silver hybrid nanoparticles with high performance magnetic hyperthermia.

    PubMed

    Ding, Qi; Liu, Dongfang; Guo, Dawei; Yang, Fang; Pang, Xingyun; Che, Renchao; Zhou, Naizhen; Xie, Jun; Sun, Jianfei; Huang, Zhihai; Gu, Ning

    2017-04-01

    Superparamagnetic Fe3O4 nanoparticles (NPs)-based hyperthermia is a promising non-invasive approach for cancer therapy. However, the heat transfer efficiency of Fe3O4 NPs is relative low, which hinders their practical clinical applications. Therefore, it is promising to improve the magnetic hyperthermia efficiency by exploring the higher performance magnetic NPs-based hybrid nanostructures. In the current study, it presents a straightforward in situ reduction method for the shape-controlled preparation of magnetite (Fe3O4) silver (Ag) hybrid NPs designed as magnetic hyperthermia heat mediators. The magnetite silver hybrid NPs with core-shell (Fe3O4@Ag) or heteromer (Fe3O4-Ag) structures exhibited a higher biocompatibility with SMMC-7721 cells and L02 cells than the individual Ag NPs. Importantly, in the magnetic hyperthermia, with the exposure to alternating current magnetic field, the Fe3O4@Ag and Fe3O4-Ag hybrid NPs indicated much better tumor suppression effect against SMMC-7721 cells than the individual Fe3O4 NPs in vitro and in vivo. These results demonstrate that the hybridisation of Fe3O4 and Ag NPs could greatly enhance the magnetic hyperthermia efficiency of Fe3O4 NPs. Therefore, the Fe3O4@Ag and Fe3O4-Ag hybrid NPs can be used to be as high performance magnetic hyperthermia mediators based on a simple and effective preparation approach.

  4. The general theory of convolutional codes

    NASA Technical Reports Server (NTRS)

    Mceliece, R. J.; Stanley, R. P.

    1993-01-01

    This article presents a self-contained introduction to the algebraic theory of convolutional codes. This introduction is partly a tutorial, but at the same time contains a number of new results which will prove useful for designers of advanced telecommunication systems. Among the new concepts introduced here are the Hilbert series for a convolutional code and the class of compact codes.

  5. Achieving unequal error protection with convolutional codes

    NASA Technical Reports Server (NTRS)

    Mills, D. G.; Costello, D. J., Jr.; Palazzo, R., Jr.

    1994-01-01

    This paper examines the unequal error protection capabilities of convolutional codes. Both time-invariant and periodically time-varying convolutional encoders are examined. The effective free distance vector is defined and is shown to be useful in determining the unequal error protection (UEP) capabilities of convolutional codes. A modified transfer function is used to determine an upper bound on the bit error probabilities for individual input bit positions in a convolutional encoder. The bound is heavily dependent on the individual effective free distance of the input bit position. A bound relating two individual effective free distances is presented. The bound is a useful tool in determining the maximum possible disparity in individual effective free distances of encoders of specified rate and memory distribution. The unequal error protection capabilities of convolutional encoders of several rates and memory distributions are determined and discussed.

  6. Cost-Benefit Analysis for the Advanced Near Net Shape Technology (ANNST) Method for Fabricating Stiffened Cylinders

    NASA Technical Reports Server (NTRS)

    Stoner, Mary Cecilia; Hehir, Austin R.; Ivanco, Marie L.; Domack, Marcia S.

    2016-01-01

    This cost-benefit analysis assesses the benefits of the Advanced Near Net Shape Technology (ANNST) manufacturing process for fabricating integrally stiffened cylinders. These preliminary, rough order-of-magnitude results report a 46 to 58 percent reduction in production costs and a 7-percent reduction in weight over the conventional metallic manufacturing technique used in this study for comparison. Production cost savings of 35 to 58 percent were reported over the composite manufacturing technique used in this study for comparison; however, the ANNST concept was heavier. In this study, the predicted return on investment of equipment required for the ANNST method was ten cryogenic tank barrels when compared with conventional metallic manufacturing. The ANNST method was compared with the conventional multi-piece metallic construction and composite processes for fabricating integrally stiffened cylinders. A case study compared these three alternatives for manufacturing a cylinder of specified geometry, with particular focus placed on production costs and process complexity, with cost analyses performed by the analogy and parametric methods. Furthermore, a scalability study was conducted for three tank diameters to assess the highest potential payoff of the ANNST process for manufacture of large-diameter cryogenic tanks. The analytical hierarchy process (AHP) was subsequently used with a group of selected subject matter experts to assess the value of the various benefits achieved by the ANNST method for potential stakeholders. The AHP study results revealed that decreased final cylinder mass and quality assurance were the most valued benefits of cylinder manufacturing methods, therefore emphasizing the relevance of the benefits achieved with the ANNST process for future projects.

  7. Fabrication of cyclodextrins-procainamide supramolecular self-assembly: shape-shifting of nanosheet into microtubular structure.

    PubMed

    Siva, S; Kothai Nayaki, S; Rajendiran, N

    2015-05-20

    Encapsulation behavior of α- and β-cyclodextrins (α-CD, β-CD) with procainamide hydrochloride (PCA) has been investigated by absorption, fluorescence, time-resolved fluorescence, proton nuclear magnetic resonance spectroscopy, scanning electron microscope, Fourier transform-infrared spectroscopy, differential scanning calorimetry, and powder X-ray diffraction techniques. Spectral results revealed that PCA forms 1:2 drug-CD2 inclusion complexes with CDs. Novel supramolecular self-assemblies have been fabricated by inclusion complexation of PCA with α-CD/β-CD and characterized by transmission electron microscope and micro-Raman imaging. The obtained results from transmission electron microscope indicated that PCA/α-CD complex could form nano-sized particles. However, when the macrocyclic ring with six glucose units was switched into seven glucose units, the resultant PCA/β-CD complex could be self-assembled to micro-sized tubular structures. Shape-shifting of 2D nanosheet into 1D microtube by simple rolling mechanism was analyzed. Thermodynamic parameters of inclusion process were determined by Parameter Method 3 calculations.

  8. Cost-Benefit Analysis for the Advanced Near Net Shape Technology (ANNST) Method for Fabricating Stiffened Cylinders

    NASA Technical Reports Server (NTRS)

    Ivanco, Marie L.; Domack, Marcia S.; Stoner, Mary Cecilia; Hehir, Austin R.

    2016-01-01

    Low Technology Readiness Levels (TRLs) and high levels of uncertainty make it challenging to develop cost estimates of new technologies in the R&D phase. It is however essential for NASA to understand the costs and benefits associated with novel concepts, in order to prioritize research investments and evaluate the potential for technology transfer and commercialization. This paper proposes a framework to perform a cost-benefit analysis of a technology in the R&D phase. This framework was developed and used to assess the Advanced Near Net Shape Technology (ANNST) manufacturing process for fabricating integrally stiffened cylinders. The ANNST method was compared with the conventional multi-piece metallic construction and composite processes for fabricating integrally stiffened cylinders. Following the definition of a case study for a cryogenic tank cylinder of specified geometry, data was gathered through interviews with Subject Matter Experts (SMEs), with particular focus placed on production costs and process complexity. This data served as the basis to produce process flowcharts and timelines, mass estimates, and rough order-of-magnitude cost and schedule estimates. The scalability of the results was subsequently investigated to understand the variability of the results based on tank size. Lastly, once costs and benefits were identified, the Analytic Hierarchy Process (AHP) was used to assess the relative value of these achieved benefits for potential stakeholders. These preliminary, rough order-of-magnitude results predict a 46 to 58 percent reduction in production costs and a 7-percent reduction in weight over the conventional metallic manufacturing technique used in this study for comparison. Compared to the composite manufacturing technique, these results predict cost savings of 35 to 58 percent; however, the ANNST concept was heavier. In this study, the predicted return on investment of equipment required for the ANNST method was ten cryogenic tank barrels

  9. Parallel architectures for computing cyclic convolutions

    NASA Technical Reports Server (NTRS)

    Yeh, C.-S.; Reed, I. S.; Truong, T. K.

    1983-01-01

    In the paper two parallel architectural structures are developed to compute one-dimensional cyclic convolutions. The first structure is based on the Chinese remainder theorem and Kung's pipelined array. The second structure is a direct mapping from the mathematical definition of a cyclic convolution to a computational architecture. To compute a d-point cyclic convolution the first structure needs d/2 inner product cells, while the second structure and Kung's linear array require d cells. However, to compute a cyclic convolution, the second structure requires less time than both the first structure and Kung's linear array. Another application of the second structure is to multiply a Toeplitz matrix by a vector. A table is listed to compare these two structures and Kung's linear array. Both structures are simple and regular and are therefore suitable for VLSI implementation.

  10. Coset Codes Viewed as Terminated Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Fossorier, Marc P. C.; Lin, Shu

    1996-01-01

    In this paper, coset codes are considered as terminated convolutional codes. Based on this approach, three new general results are presented. First, it is shown that the iterative squaring construction can equivalently be defined from a convolutional code whose trellis terminates. This convolutional code determines a simple encoder for the coset code considered, and the state and branch labelings of the associated trellis diagram become straightforward. Also, from the generator matrix of the code in its convolutional code form, much information about the trade-off between the state connectivity and complexity at each section, and the parallel structure of the trellis, is directly available. Based on this generator matrix, it is shown that the parallel branches in the trellis diagram of the convolutional code represent the same coset code C(sub 1), of smaller dimension and shorter length. Utilizing this fact, a two-stage optimum trellis decoding method is devised. The first stage decodes C(sub 1), while the second stage decodes the associated convolutional code, using the branch metrics delivered by stage 1. Finally, a bidirectional decoding of each received block starting at both ends is presented. If about the same number of computations is required, this approach remains very attractive from a practical point of view as it roughly doubles the decoding speed. This fact is particularly interesting whenever the second half of the trellis is the mirror image of the first half, since the same decoder can be implemented for both parts.

  11. Design and fabrication of 3D-printed anatomically shaped lumbar cage for intervertebral disc (IVD) degeneration treatment.

    PubMed

    Serra, T; Capelli, C; Toumpaniari, R; Orriss, I R; Leong, J J H; Dalgarno, K; Kalaskar, D M

    2016-07-19

    Spinal fusion is the gold standard surgical procedure for degenerative spinal conditions when conservative therapies have been unsuccessful in rehabilitation of patients. Novel strategies are required to improve biocompatibility and osseointegration of traditionally used materials for lumbar cages. Furthermore, new design and technologies are needed to bridge the gap due to the shortage of optimal implant sizes to fill the intervertebral disc defect. Within this context, additive manufacturing technology presents an excellent opportunity to fabricate ergonomic shape medical implants. The goal of this study is to design and manufacture a 3D-printed lumbar cage for lumbar interbody fusion. Optimisations of the proposed implant design and its printing parameters were achieved via in silico analysis. The final construct was characterised via scanning electron microscopy, contact angle, x-ray micro computed tomography (μCT), atomic force microscopy, and compressive test. Preliminary in vitro cell culture tests such as morphological assessment and metabolic activities were performed to access biocompatibility of 3D-printed constructs. Results of in silico analysis provided a useful platform to test preliminary cage design and to find an optimal value of filling density for 3D printing process. Surface characterisation confirmed a uniform coating of nHAp with nanoscale topography. Mechanical evaluation showed mechanical properties of final cage design similar to that of trabecular bone. Preliminary cell culture results showed promising results in terms of cell growth and activity confirming biocompatibility of constructs. Thus for the first time, design optimisation based on computational and experimental analysis combined with the 3D-printing technique for intervertebral fusion cage has been reported in a single study. 3D-printing is a promising technique for medical applications and this study paves the way for future development of customised implants in spinal

  12. Radiative and convective properties of 316L Stainless Steel fabricated using the Laser Engineered Net Shaping process

    NASA Astrophysics Data System (ADS)

    Knopp, Jonathan

    Temperature evolution of metallic materials during the additive manufacturing process has direct influence in determining the materials microstructure and resultant characteristics. Through the power of Infrared (IR) thermography it is now possible to monitor thermal trends in a build structure, giving the power to adjust building parameters in real time. The IR camera views radiation in the IR wavelengths and determines temperature of an object by the amount of radiation emitted from the object in those wavelengths. Determining the amount of radiation emitted from the material, known as a materials emissivity, can be difficult in that emissivity is affected by both temperature and surface finish. It has been shown that the use of a micro-blackbody cavity can be used as an accurate reference temperature when the sample is held at thermal equilibrium. A micro-blackbody cavity was created in a sample of 316L Stainless Steel after being fabricated during using the Laser Engineered Net Shaping (LENS) process. Holding the sample at thermal equilibrium and using the micro-blackbody cavity as a reference and thermocouple as a second reference emissivity values were able to be obtained. IR thermography was also used to observe the manufacturing of these samples. When observing the IR thermography, patterns in the thermal history of the build were shown to be present as well as distinct cooling rates of the material. This information can be used to find true temperatures of 316L Stainless Steel during the LENS process for better control of desired material properties as well as future work in determining complete energy balance.

  13. Fabrication of transparent, tough, and conductive shape-memory polyurethane films by incorporating a small amount of high-quality graphene.

    PubMed

    Jung, Yong Chae; Kim, Jin Hee; Hayashi, Takuya; Kim, Yoong Ahm; Endo, Morinobu; Terrones, Mauricio; Dresselhaus, Mildred S

    2012-04-23

    We report a mechanically strong, electrically and thermally conductive, and optically transparent shape-memory polyurethane composite which was fabricated by introducing a small amount (0.1 wt%) of high-quality graphene as a filler. Geometrically large (≈4.6 μm(2)), but highly crystallized few-layer graphenes, verified by Raman spectroscopy and transmission electron microscopy, were prepared by the sonication of expandable graphite in an organic solvent. Oxygen- containing functional groups at the edge plane of graphene were crucial for an effective stress transfer from the graphene to polyurethane. Homogeneously dispersed few-layered graphene enabled polyurethane to have a high shape recovery force of 1.8 MPa cm(-3). Graphene, which is intrinsically stretchable up to 10%, will enable high-performance composites to be fabricated at relatively low cost and we thus envisage that such composites may replace carbon nanotubes for various applications in the near future.

  14. Dip TIPS as a Facile and Versatile Method for Fabrication of Polymer Foams with Controlled Shape, Size and Pore Architecture for Bioengineering Applications

    PubMed Central

    Kasoju, Naresh; Kubies, Dana; Kumorek, Marta M.; Kříž, Jan; Fábryová, Eva; Machová, Lud'ka; Kovářová, Jana; Rypáček, František

    2014-01-01

    The porous polymer foams act as a template for neotissuegenesis in tissue engineering, and, as a reservoir for cell transplants such as pancreatic islets while simultaneously providing a functional interface with the host body. The fabrication of foams with the controlled shape, size and pore structure is of prime importance in various bioengineering applications. To this end, here we demonstrate a thermally induced phase separation (TIPS) based facile process for the fabrication of polymer foams with a controlled architecture. The setup comprises of a metallic template bar (T), a metallic conducting block (C) and a non-metallic reservoir tube (R), connected in sequence T-C-R. The process hereinafter termed as Dip TIPS, involves the dipping of the T-bar into a polymer solution, followed by filling of the R-tube with a freezing mixture to induce the phase separation of a polymer solution in the immediate vicinity of T-bar; Subsequent free-drying or freeze-extraction steps produced the polymer foams. An easy exchange of the T-bar of a spherical or rectangular shape allowed the fabrication of tubular, open- capsular and flat-sheet shaped foams. A mere change in the quenching time produced the foams with a thickness ranging from hundreds of microns to several millimeters. And, the pore size was conveniently controlled by varying either the polymer concentration or the quenching temperature. Subsequent in vivo studies in brown Norway rats for 4-weeks demonstrated the guided cell infiltration and homogenous cell distribution through the polymer matrix, without any fibrous capsule and necrotic core. In conclusion, the results show the “Dip TIPS” as a facile and adaptable process for the fabrication of anisotropic channeled porous polymer foams of various shapes and sizes for potential applications in tissue engineering, cell transplantation and other related fields. PMID:25275373

  15. Fabrication and characterization of a foamed polylactic acid (PLA)/ thermoplastic polyurethane (TPU) shape memory polymer (SMP) blend for biomedical and clinical applications

    NASA Astrophysics Data System (ADS)

    Song, Janice J.; Srivastava, Ijya; Kowalski, Jennifer; Naguib, Hani E.

    2014-03-01

    Shape memory polymers (SMP) are a class of stimuli-responsive materials that are able to respond to external stimulus such as heat by altering their shape. Bio-compatible SMPs have a number of advantages over static materials and are being studied extensively for biomedical and clinical applications (such as tissue stents and scaffolds). A previous study has demonstrated that the bio-compatible polymer blend of polylactic acid (PLA)/ thermoplastic polyurethane (TPU) (50/50 and 70/30) exhibit good shape memory properties. In this study, the mechanical and thermo-mechanical (shape memory) properties of TPU/PLA SMP blends were characterized; the compositions studied were 80/20, 65/35, and 50/50 TPU/PLA. In addition, porous TPU/PLA SMP blends were fabricated with a gas-foaming technique; and the morphology of the porous structure of these SMPs foams were characterized with scanning electron microscopy (SEM). The TPU/PLA bio-compatible SMP blend was fabricated with melt-blending and compression molding. The glass transition temperature (Tg) of the SMP blends was determined with a differential scanning calorimeter (DSC). The mechanical properties studied were the stress-strain behavior, tensile strength, and elastic modulus; and the thermomechanical (or shape memory) properties studied were the shape fixity rate (Rf), shape recovery rate (Rr), response time, and the effect of recovery temperature on Rr. The porous 80/20 PLA/TPU SMP blend was found to have the highest tensile strength, toughness and percentage extension, as well as the lowest density and uniform pore structure in the micron and submicron scale. The porous 80/20 TPU/PLA SMP blend may be further developed for specific biomedical and clinical applications where a combination of tensile strength, toughness, and low density are required.

  16. Molecular graph convolutions: moving beyond fingerprints.

    PubMed

    Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

    2016-08-01

    Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph-atoms, bonds, distances, etc.-which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

  17. Cyclic Cocycles on Twisted Convolution Algebras

    NASA Astrophysics Data System (ADS)

    Angel, Eitan

    2013-01-01

    We give a construction of cyclic cocycles on convolution algebras twisted by gerbes over discrete translation groupoids. For proper étale groupoids, Tu and Xu (Adv Math 207(2):455-483, 2006) provide a map between the periodic cyclic cohomology of a gerbe-twisted convolution algebra and twisted cohomology groups which is similar to the construction of Mathai and Stevenson (Adv Math 200(2):303-335, 2006). When the groupoid is not proper, we cannot construct an invariant connection on the gerbe; therefore to study this algebra, we instead develop simplicial techniques to construct a simplicial curvature 3-form representing the class of the gerbe. Then by using a JLO formula we define a morphism from a simplicial complex twisted by this simplicial curvature 3-form to the mixed bicomplex computing the periodic cyclic cohomology of the twisted convolution algebras.

  18. Molecular graph convolutions: moving beyond fingerprints

    NASA Astrophysics Data System (ADS)

    Kearnes, Steven; McCloskey, Kevin; Berndl, Marc; Pande, Vijay; Riley, Patrick

    2016-08-01

    Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-driven decisions. We describe molecular graph convolutions, a machine learning architecture for learning from undirected graphs, specifically small molecules. Graph convolutions use a simple encoding of the molecular graph—atoms, bonds, distances, etc.—which allows the model to take greater advantage of information in the graph structure. Although graph convolutions do not outperform all fingerprint-based methods, they (along with other graph-based methods) represent a new paradigm in ligand-based virtual screening with exciting opportunities for future improvement.

  19. Astronomical Image Subtraction by Cross-Convolution

    NASA Astrophysics Data System (ADS)

    Yuan, Fang; Akerlof, Carl W.

    2008-04-01

    In recent years, there has been a proliferation of wide-field sky surveys to search for a variety of transient objects. Using relatively short focal lengths, the optics of these systems produce undersampled stellar images often marred by a variety of aberrations. As participants in such activities, we have developed a new algorithm for image subtraction that no longer requires high-quality reference images for comparison. The computational efficiency is comparable with similar procedures currently in use. The general technique is cross-convolution: two convolution kernels are generated to make a test image and a reference image separately transform to match as closely as possible. In analogy to the optimization technique for generating smoothing splines, the inclusion of an rms width penalty term constrains the diffusion of stellar images. In addition, by evaluating the convolution kernels on uniformly spaced subimages across the total area, these routines can accommodate point-spread functions that vary considerably across the focal plane.

  20. Recent Advances in Near-Net-Shape Fabrication of Al-Li Alloy 2195 for Launch Vehicles

    NASA Technical Reports Server (NTRS)

    Wagner, John; Domack, Marcia; Hoffman, Eric

    2007-01-01

    Recent applications in launch vehicles use 2195 processed to Super Lightweight Tank specifications. Potential benefits exist by tailoring heat treatment and other processing parameters to the application. Assess the potential benefits and advocate application of Al-Li near-net-shape technologies for other launch vehicle structural components. Work with manufacturing and material producers to optimize Al-Li ingot shape and size for enhanced near-net-shape processing. Examine time dependent properties of 2195 critical for reusable applications.

  1. Differences in the thickness of mouthguards fabricated from ethylene vinyl acetate copolymer sheets with differently arranged v-shaped grooves: part 2 - effect of shape on the working model.

    PubMed

    Takahashi, Mutsumi; Koide, Kaoru; Mizuhashi, Fumi

    2014-12-01

    The aim of this study was to evaluate the change in thickness of a working model mouthguard sheet due to different shape. Mouthguards were fabricated with ethylene vinyl acetate (EVA) sheets (4.0 mm thick) using a vacuum-forming machine. Two shapes of the sheet were compared: normal sheet or v-shaped groove 10-40 mm from the anterior end. Additionally, two shapes of the working model were compared; the basal plane was vertical to the tooth axis of the maxillary central incisor (condition A), and the occlusal plane was parallel to the basal plane (condition B). Sheets were heated until they sagged 15 mm below the clamp. Postmolding thickness was determined for the incisal portion (incisal edge and labial surface) and molar portion (cusp and buccal surface). Differences in the change in thickness due to the shape of the sheets and model were analyzed using two-way anova followed by a Bonferroni's multiple comparison tests. The thickness of the mouthguard sheet with v-shaped grooves was more than that of the normal sheet at all measuring points under condition A and condition B (P < 0.01). The thickness of condition B was less than that of condition A, there the incisal portion in the normal sheet and the incisal edge in the sheet with v-shaped grooves (P < 0.01). The present results suggested that thickness after molding was secured by the use of the sheet with v-shaped grooves. In particular, the model with the undercut on the labial surface may be clinically useful.

  2. Sequential Syndrome Decoding of Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    The algebraic structure of convolutional codes are reviewed and sequential syndrome decoding is applied to those codes. These concepts are then used to realize by example actual sequential decoding, using the stack algorithm. The Fano metric for use in sequential decoding is modified so that it can be utilized to sequentially find the minimum weight error sequence.

  3. Effectiveness of Convolutional Code in Multipath Underwater Acoustic Channel

    NASA Astrophysics Data System (ADS)

    Park, Jihyun; Seo, Chulwon; Park, Kyu-Chil; Yoon, Jong Rak

    2013-07-01

    The forward error correction (FEC) is achieved by increasing redundancy of information. Convolutional coding with Viterbi decoding is a typical FEC technique in channel corrupted by additive white gaussian noise. But the FEC effectiveness of convolutional code is questioned in multipath frequency selective fading channel. In this paper, how convolutional code works in multipath channel in underwater, is examined. Bit error rates (BER) with and without 1/2 convolutional code are analyzed based on channel bandwidth which is frequency selectivity parameter. It is found that convolution code performance is well matched in non selective channel and also effective in selective channel.

  4. Cell differentiation on disk- and string-shaped hydrogels fabricated from Ca(2+) -responsive self-assembling peptides.

    PubMed

    Fukunaga, Kazuto; Tsutsumi, Hiroshi; Mihara, Hisakazu

    2016-11-04

    We recently developed a self-assembling peptide, E1Y9, that self-assembles into nanofibers and forms a hydrogel in the presence of Ca(2+) . E1Y9 derivatives conjugated with functional peptide sequences derived from extracellular matrices (ECMs) reportedly self-assemble into peptide nanofibers that enhance cell adhesion and differentiation. In this study, E1Y9/E1Y9-IKVAV-mixed hydrogels were constructed to serve as artificial ECMs that promote cell differentiation. E1Y9 and E1Y9-IKVAV co-assembled into networked nanofibers, and hydrogels with disk and string shapes were formed in response to Ca(2+) treatment. The neuronal differentiation of PC12 cells was facilitated on hydrogels of both shapes that contained the IKVAV motifs. Moreover, long neurites extended along the long axis of the string-shaped gel, suggesting that the structure of hydrogels of this shape can affect cellular orientation. Thus, E1Y9 hydrogels can potentially be used as artificial ECMs with desirable bioactivities and shapes that could be useful in tissue engineering applications. © 2015 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 106: 476-483, 2016.

  5. Design and fabrication of a novel XYθz monolithic micro-positioning stage driven by NiTi shape-memory-alloy actuators

    NASA Astrophysics Data System (ADS)

    AbuZaiter, Alaa; Faris Hikmat, Omer; Nafea, Marwan; Ali, Mohamed Sultan Mohamed

    2016-10-01

    This paper reports a new shape-memory-alloy (SMA) micro-positioning stage. The device has been monolithically micro-machined with a single fabrication step. The design comprises a moving stage that is manipulated by six SMA planar springs actuators to generate movements with three degrees of freedom. The overall design is square in shape and has dimensions of 12 mm × 12 mm × 0.25 mm. Localized thermomechanical training for shape setting of SMA planar springs was performed using electrical current induced heating at restrained condition to individually train each of the six actuators to memorize a predetermined shape. For actuation, each SMA actuator is individually driven using Joule heating induced by an electrical current. The current flow is controlled by an external pulse-width modulation signal. The thermal response and heat distribution were simulated and experimentally verified using infrared imaging. The micro-positioning results indicated maximum stage movements of 1.2 and 1.6 mm along the x- and y-directions, respectively. Rotational movements were also demonstrated with a total range of 20°. The developed micro-positioning device has been successfully used to move a small object for microscopic scanning applications.

  6. Digital Correlation By Optical Convolution/Correlation

    NASA Astrophysics Data System (ADS)

    Trimble, Joel; Casasent, David; Psaltis, Demetri; Caimi, Frank; Carlotto, Mark; Neft, Deborah

    1980-12-01

    Attention is given to various methods by which the accuracy achieveable and the dynamic range requirements of an optical computer can be enhanced. A new time position coding acousto-optic technique for optical residue arithmetic processing is presented and experimental demonstration is included. Major attention is given to the implementation of a correlator operating on digital or decimal encoded signals. Using a convolution description of multiplication, we realize such a correlator by optical convolution in one dimension and optical correlation in the other dimension of a optical system. A coherent matched spatial filter system operating on digital encoded signals, a noncoherent processor operating on complex-valued digital-encoded data, and a real-time multi-channel acousto-optic system for such operations are described and experimental verifications are included.

  7. Performance of convolutionally coded unbalanced QPSK systems

    NASA Technical Reports Server (NTRS)

    Divsalar, D.; Yuen, J. H.

    1980-01-01

    An evaluation is presented of the performance of three representative convolutionally coded unbalanced quadri-phase-shift-keying (UQPSK) systems in the presence of noisy carrier reference and crosstalk. The use of a coded UQPSK system for transmitting two telemetry data streams with different rates and different powers has been proposed for the Venus Orbiting Imaging Radar mission. Analytical expressions for bit error rates in the presence of a noisy carrier phase reference are derived for three representative cases: (1) I and Q channels are coded independently; (2) I channel is coded, Q channel is uncoded; and (3) I and Q channels are coded by a common 1/2 code. For rate 1/2 convolutional codes, QPSK modulation can be used to reduce the bandwidth requirement.

  8. A convolutional neural network neutrino event classifier

    DOE PAGES

    Aurisano, A.; Radovic, A.; Rocco, D.; ...

    2016-09-01

    Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology withoutmore » the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.« less

  9. A convolutional neural network neutrino event classifier

    SciTech Connect

    Aurisano, A.; Radovic, A.; Rocco, D.; Himmel, A.; Messier, M. D.; Niner, E.; Pawloski, G.; Psihas, F.; Sousa, A.; Vahle, P.

    2016-09-01

    Here, convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  10. A Construction of MDS Quantum Convolutional Codes

    NASA Astrophysics Data System (ADS)

    Zhang, Guanghui; Chen, Bocong; Li, Liangchen

    2015-09-01

    In this paper, two new families of MDS quantum convolutional codes are constructed. The first one can be regarded as a generalization of [36, Theorem 6.5], in the sense that we do not assume that q≡1 (mod 4). More specifically, we obtain two classes of MDS quantum convolutional codes with parameters: (i) [( q 2+1, q 2-4 i+3,1;2,2 i+2)] q , where q≥5 is an odd prime power and 2≤ i≤( q-1)/2; (ii) , where q is an odd prime power with the form q=10 m+3 or 10 m+7 ( m≥2), and 2≤ i≤2 m-1.

  11. A convolutional neural network neutrino event classifier

    NASA Astrophysics Data System (ADS)

    Aurisano, A.; Radovic, A.; Rocco, D.; Himmel, A.; Messier, M. D.; Niner, E.; Pawloski, G.; Psihas, F.; Sousa, A.; Vahle, P.

    2016-09-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  12. Deep Learning with Hierarchical Convolutional Factor Analysis

    PubMed Central

    Chen, Bo; Polatkan, Gungor; Sapiro, Guillermo; Blei, David; Dunson, David; Carin, Lawrence

    2013-01-01

    Unsupervised multi-layered (“deep”) models are considered for general data, with a particular focus on imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis, that explicitly exploit the convolutional nature of the expansion. In order to address large-scale and streaming data, an online version of VB is also developed. The number of basis functions or dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature. PMID:23787342

  13. Hydrothermal fabrication of octahedral-shaped Fe{sub 3}O{sub 4} nanoparticles and their magnetorheological response

    SciTech Connect

    Jung, H. S.; Choi, H. J.

    2015-05-07

    Octahedral-shaped Fe{sub 3}O{sub 4} nanoparticles were synthesized in the presence of 1,3-diaminopropane using a hydrothermal method and assessed as a potential magnetorheological (MR) material. Their morphology, crystal structure, and magnetic properties were examined by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, and vibrating sample magnetometry, respectively. The MR characteristics of the octahedral-shaped, Fe{sub 3}O{sub 4} nanoparticle-based MR particles when dispersed in silicone oil with a 10 vol. % particle concentration were examined using a rotational rheometer under an external magnetic field. The resulting MR fluids exhibited a Bingham-like behavior with a distinctive yield stress from their flow curves.

  14. Novel fabrication process for 3D meander-shaped microcoils in SU-8 dielectric and their application to linear micromotors

    NASA Astrophysics Data System (ADS)

    Seidemann, Volker; Buettgenbach, Stephanus

    2001-04-01

    This paper reports on an optimized fabrication process for three dimensional coil structures such as meander or helical coils wound around in plane magnetic structures. The process consists of UV depth lithography employing AZ4562 and SU8 photo resists and electroplating of copper and nickel-iron. Furthermore SU8 is used as the embedding dielectric due to its excellent planarization properties and high structural aspect ratio. Special emphasis was laid on the decrease of via interconnect resistance by electroplating the vias and upper conductors in a single step thus avoiding a large number of resistive interfaces. This was achieved by sacrificial wiring and structured seed layers. The developed technology is applied to a variable reluctance micro motor with a novel design that avoids high friction. The presented concept makes use of a stator traveler configuration generating complementary attraction forces. The technology and design concept is presented and first results are demonstrated.

  15. Manifestation of the shape-memory effect in polyetherurethane cellular plastics, fabric composites, and sandwich structures under microgravity

    NASA Astrophysics Data System (ADS)

    Babaevskii, P. G.; Kozlov, N. A.; Agapov, I. G.; Reznichenko, G. M.; Churilo, N. V.; Churilo, I. V.

    2016-09-01

    The results of experiments that were performed to test the feasibility of creating sandwich structures (consisting of thin-layer sheaths of polymer composites and a cellular polymer core) with the shapememory effect as models of the transformable components of space structures have been given. The data obtained indicate that samples of sandwich structures under microgravity conditions on board the International Space Station have recovered their shape to almost the same degree as under terrestrial conditions, which makes it possible to recommend them for creating components of transformable space structures on their basis.

  16. The analysis of convolutional codes via the extended Smith algorithm

    NASA Technical Reports Server (NTRS)

    Mceliece, R. J.; Onyszchuk, I.

    1993-01-01

    Convolutional codes have been the central part of most error-control systems in deep-space communication for many years. Almost all such applications, however, have used the restricted class of (n,1), also known as 'rate 1/n,' convolutional codes. The more general class of (n,k) convolutional codes contains many potentially useful codes, but their algebraic theory is difficult and has proved to be a stumbling block in the evolution of convolutional coding systems. In this article, the situation is improved by describing a set of practical algorithms for computing certain basic things about a convolutional code (among them the degree, the Forney indices, a minimal generator matrix, and a parity-check matrix), which are usually needed before a system using the code can be built. The approach is based on the classic Forney theory for convolutional codes, together with the extended Smith algorithm for polynomial matrices, which is introduced in this article.

  17. Fabrication of nano-imprint templates for dual-Damascene applications using a high resolution variable shape E-beam writer

    NASA Astrophysics Data System (ADS)

    Pritschow, Marcus; Butschke, Joerg; Irmscher, Mathias; Sailer, Holger; Resnick, Douglas; Thompson, Ecron

    2007-10-01

    A 3D template fabrication process has been developed, which enables the generation of high resolution, high aspect pillars on top of lines. These templates will be used to print both vias and metal lines at once for the dual damascene technology. Due to the complexity of state of the art CMOS designs only a variable shape e-beam (VSB) writer combined with chemically amplified resists (CAR) can be considered for the patterning process. We focused our work especially on the generation of high aspect pillars with a diameter below 50nm and the development of suitable overlay strategies for getting a precise alignment between the two template tiers. In this context we investigated the influence of exposure strategies on the overlay result across the entire imprint area of 25mm × 25mm. Finally, we realized templates according to the MII standard with different test designs and confirmed printability of one of them on a MII tool.

  18. A fast complex integer convolution using a hybrid transform

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; K Truong, T.

    1978-01-01

    It is shown that the Winograd transform can be combined with a complex integer transform over the Galois field GF(q-squared) to yield a new algorithm for computing the discrete cyclic convolution of complex number points. By this means a fast method for accurately computing the cyclic convolution of a sequence of complex numbers for long convolution lengths can be obtained. This new hybrid algorithm requires fewer multiplications than previous algorithms.

  19. Flexible Fabrication of Shape-Controlled Collagen Building Blocks for Self-Assembly of 3D Microtissues.

    PubMed

    Zhang, Xu; Meng, Zhaoxu; Ma, Jingyun; Shi, Yang; Xu, Hui; Lykkemark, Simon; Qin, Jianhua

    2015-08-12

    Creating artificial tissue-like structures that possess the functionality, specificity, and architecture of native tissues remains a big challenge. A new and straightforward strategy for generating shape-controlled collagen building blocks with a well-defined architecture is presented, which can be used for self-assembly of complex 3D microtissues. Collagen blocks with tunable geometries are controllably produced and released via a membrane-templated microdevice. The formation of functional microtissues by embedding tissue-specific cells into collagen blocks with expression of specific proteins is described. The spontaneous self-assembly of cell-laden collagen blocks into organized tissue constructs with predetermined configurations is demonstrated, which are largely driven by the synergistic effects of cell-cell and cell-matrix interactions. This new strategy would open up new avenues for the study of tissue/organ morphogenesis, and tissue engineering applications.

  20. Fused Deposition Modeling Enables the Low-Cost Fabrication of Porous, Customized-Shape Sorbents for Small-Molecule Extraction.

    PubMed

    Belka, Mariusz; Ulenberg, Szymon; Bączek, Tomasz

    2017-04-07

    Fused deposition modeling, one of the most common techniques in three-dimensional printing and additive manufacturing, has many practical applications in the fields of chemistry and pharmacy. We demonstrate that a thermoplastic elastomer-poly(vinyl alcohol) (PVA) composite material (LAY-FOMM 60), which presents porous properties after PVA removal, is useful for the extraction of small-molecule drug-like compounds from water samples. The usefulness of the proposed approach is demonstrated by the extraction of glimepiride from a water sample, followed by LC-MS analysis. The recovery was 82.24%, with a relative standard deviation of less than 5%. The proposed approach can change the way of thinking about extraction and sample preparation due to a shift to the use of sorbents with customizable size, shape, and chemical properties that do not rely on commercial suppliers.

  1. Human Parsing with Contextualized Convolutional Neural Network.

    PubMed

    Liang, Xiaodan; Xu, Chunyan; Shen, Xiaohui; Yang, Jianchao; Tang, Jinhui; Lin, Liang; Yan, Shuicheng

    2016-03-02

    In this work, we address the human parsing task with a novel Contextualized Convolutional Neural Network (Co-CNN) architecture, which well integrates the cross-layer context, global image-level context, semantic edge context, within-super-pixel context and cross-super-pixel neighborhood context into a unified network. Given an input human image, Co-CNN produces the pixel-wise categorization in an end-to-end way. First, the cross-layer context is captured by our basic local-to-global-to-local structure, which hierarchically combines the global semantic information and the local fine details across different convolutional layers. Second, the global image-level label prediction is used as an auxiliary objective in the intermediate layer of the Co-CNN, and its outputs are further used for guiding the feature learning in subsequent convolutional layers to leverage the global imagelevel context. Third, semantic edge context is further incorporated into Co-CNN, where the high-level semantic boundaries are leveraged to guide pixel-wise labeling. Finally, to further utilize the local super-pixel contexts, the within-super-pixel smoothing and cross-super-pixel neighbourhood voting are formulated as natural sub-components of the Co-CNN to achieve the local label consistency in both training and testing process. Comprehensive evaluations on two public datasets well demonstrate the significant superiority of our Co-CNN over other state-of-the-arts for human parsing. In particular, the F-1 score on the large dataset [1] reaches 81:72% by Co-CNN, significantly higher than 62:81% and 64:38% by the state-of-the-art algorithms, MCNN [2] and ATR [1], respectively. By utilizing our newly collected large dataset for training, our Co-CNN can achieve 85:36% in F-1 score.

  2. Applications of convolution voltammetry in electroanalytical chemistry.

    PubMed

    Bentley, Cameron L; Bond, Alan M; Hollenkamp, Anthony F; Mahon, Peter J; Zhang, Jie

    2014-02-18

    The robustness of convolution voltammetry for determining accurate values of the diffusivity (D), bulk concentration (C(b)), and stoichiometric number of electrons (n) has been demonstrated by applying the technique to a series of electrode reactions in molecular solvents and room temperature ionic liquids (RTILs). In acetonitrile, the relatively minor contribution of nonfaradaic current facilitates analysis with macrodisk electrodes, thus moderate scan rates can be used without the need to perform background subtraction to quantify the diffusivity of iodide [D = 1.75 (±0.02) × 10(-5) cm(2) s(-1)] in this solvent. In the RTIL 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide, background subtraction is necessary at a macrodisk electrode but can be avoided at a microdisk electrode, thereby simplifying the analytical procedure and allowing the diffusivity of iodide [D = 2.70 (±0.03) × 10(-7) cm(2) s(-1)] to be quantified. Use of a convolutive procedure which simultaneously allows D and nC(b) values to be determined is also demonstrated. Three conditions under which a technique of this kind may be applied are explored and are related to electroactive species which display slow dissolution kinetics, undergo a single multielectron transfer step, or contain multiple noninteracting redox centers using ferrocene in an RTIL, 1,4-dinitro-2,3,5,6-tetramethylbenzene, and an alkynylruthenium trimer, respectively, as examples. The results highlight the advantages of convolution voltammetry over steady-state techniques such as rotating disk electrode voltammetry and microdisk electrode voltammetry, as it is not restricted by the mode of diffusion (planar or radial), hence removing limitations on solvent viscosity, electrode geometry, and voltammetric scan rate.

  3. Bacterial colony counting by Convolutional Neural Networks.

    PubMed

    Ferrari, Alessandro; Lombardi, Stefano; Signoroni, Alberto

    2015-01-01

    Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.

  4. Zebrafish tracking using convolutional neural networks

    PubMed Central

    XU, Zhiping; Cheng, Xi En

    2017-01-01

    Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable. PMID:28211462

  5. Convolutional coding combined with continuous phase modulation

    NASA Technical Reports Server (NTRS)

    Pizzi, S. V.; Wilson, S. G.

    1985-01-01

    Background theory and specific coding designs for combined coding/modulation schemes utilizing convolutional codes and continuous-phase modulation (CPM) are presented. In this paper the case of r = 1/2 coding onto a 4-ary CPM is emphasized, with short-constraint length codes presented for continuous-phase FSK, double-raised-cosine, and triple-raised-cosine modulation. Coding buys several decibels of coding gain over the Gaussian channel, with an attendant increase of bandwidth. Performance comparisons in the power-bandwidth tradeoff with other approaches are made.

  6. Zebrafish tracking using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Xu, Zhiping; Cheng, Xi En

    2017-02-01

    Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.

  7. QCDNUM: Fast QCD evolution and convolution

    NASA Astrophysics Data System (ADS)

    Botje, M.

    2011-02-01

    The QCDNUM program numerically solves the evolution equations for parton densities and fragmentation functions in perturbative QCD. Un-polarised parton densities can be evolved up to next-to-next-to-leading order in powers of the strong coupling constant, while polarised densities or fragmentation functions can be evolved up to next-to-leading order. Other types of evolution can be accessed by feeding alternative sets of evolution kernels into the program. A versatile convolution engine provides tools to compute parton luminosities, cross-sections in hadron-hadron scattering, and deep inelastic structure functions in the zero-mass scheme or in generalised mass schemes. Input to these calculations are either the QCDNUM evolved densities, or those read in from an external parton density repository. Included in the software distribution are packages to calculate zero-mass structure functions in un-polarised deep inelastic scattering, and heavy flavour contributions to these structure functions in the fixed flavour number scheme. Program summaryProgram title: QCDNUM version: 17.00 Catalogue identifier: AEHV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Public Licence No. of lines in distributed program, including test data, etc.: 45 736 No. of bytes in distributed program, including test data, etc.: 911 569 Distribution format: tar.gz Programming language: Fortran-77 Computer: All Operating system: All RAM: Typically 3 Mbytes Classification: 11.5 Nature of problem: Evolution of the strong coupling constant and parton densities, up to next-to-next-to-leading order in perturbative QCD. Computation of observable quantities by Mellin convolution of the evolved densities with partonic cross-sections. Solution method: Parametrisation of the parton densities as linear or quadratic splines on a discrete grid, and evolution of the spline

  8. Convolutional fountain distribution over fading wireless channels

    NASA Astrophysics Data System (ADS)

    Usman, Mohammed

    2012-08-01

    Mobile broadband has opened the possibility of a rich variety of services to end users. Broadcast/multicast of multimedia data is one such service which can be used to deliver multimedia to multiple users economically. However, the radio channel poses serious challenges due to its time-varying properties, resulting in each user experiencing different channel characteristics, independent of other users. Conventional methods of achieving reliability in communication, such as automatic repeat request and forward error correction do not scale well in a broadcast/multicast scenario over radio channels. Fountain codes, being rateless and information additive, overcome these problems. Although the design of fountain codes makes it possible to generate an infinite sequence of encoded symbols, the erroneous nature of radio channels mandates the need for protecting the fountain-encoded symbols, so that the transmission is feasible. In this article, the performance of fountain codes in combination with convolutional codes, when used over radio channels, is presented. An investigation of various parameters, such as goodput, delay and buffer size requirements, pertaining to the performance of fountain codes in a multimedia broadcast/multicast environment is presented. Finally, a strategy for the use of 'convolutional fountain' over radio channels is also presented.

  9. NUCLEI SEGMENTATION VIA SPARSITY CONSTRAINED CONVOLUTIONAL REGRESSION

    PubMed Central

    Zhou, Yin; Chang, Hang; Barner, Kenneth E.; Parvin, Bahram

    2017-01-01

    Automated profiling of nuclear architecture, in histology sections, can potentially help predict the clinical outcomes. However, the task is challenging as a result of nuclear pleomorphism and cellular states (e.g., cell fate, cell cycle), which are compounded by the batch effect (e.g., variations in fixation and staining). Present methods, for nuclear segmentation, are based on human-designed features that may not effectively capture intrinsic nuclear architecture. In this paper, we propose a novel approach, called sparsity constrained convolutional regression (SCCR), for nuclei segmentation. Specifically, given raw image patches and the corresponding annotated binary masks, our algorithm jointly learns a bank of convolutional filters and a sparse linear regressor, where the former is used for feature extraction, and the latter aims to produce a likelihood for each pixel being nuclear region or background. During classification, the pixel label is simply determined by a thresholding operation applied on the likelihood map. The method has been evaluated using the benchmark dataset collected from The Cancer Genome Atlas (TCGA). Experimental results demonstrate that our method outperforms traditional nuclei segmentation algorithms and is able to achieve competitive performance compared to the state-of-the-art algorithm built upon human-designed features with biological prior knowledge. PMID:28101301

  10. Convolution Inequalities for the Boltzmann Collision Operator

    NASA Astrophysics Data System (ADS)

    Alonso, Ricardo J.; Carneiro, Emanuel; Gamba, Irene M.

    2010-09-01

    We study integrability properties of a general version of the Boltzmann collision operator for hard and soft potentials in n-dimensions. A reformulation of the collisional integrals allows us to write the weak form of the collision operator as a weighted convolution, where the weight is given by an operator invariant under rotations. Using a symmetrization technique in L p we prove a Young’s inequality for hard potentials, which is sharp for Maxwell molecules in the L 2 case. Further, we find a new Hardy-Littlewood-Sobolev type of inequality for Boltzmann collision integrals with soft potentials. The same method extends to radially symmetric, non-increasing potentials that lie in some {Ls_{weak}} or L s . The method we use resembles a Brascamp, Lieb and Luttinger approach for multilinear weighted convolution inequalities and follows a weak formulation setting. Consequently, it is closely connected to the classical analysis of Young and Hardy-Littlewood-Sobolev inequalities. In all cases, the inequality constants are explicitly given by formulas depending on integrability conditions of the angular cross section (in the spirit of Grad cut-off). As an additional application of the technique we also obtain estimates with exponential weights for hard potentials in both conservative and dissipative interactions.

  11. Experimental Investigation of Convoluted Contouring for Aircraft Afterbody Drag Reduction

    NASA Technical Reports Server (NTRS)

    Deere, Karen A.; Hunter, Craig A.

    1999-01-01

    An experimental investigation was performed in the NASA Langley 16-Foot Transonic Tunnel to determine the aerodynamic effects of external convolutions, placed on the boattail of a nonaxisymmetric nozzle for drag reduction. Boattail angles of 15 and 22 were tested with convolutions placed at a forward location upstream of the boattail curvature, at a mid location along the curvature and at a full location that spanned the entire boattail flap. Each of the baseline nozzle afterbodies (no convolutions) had a parabolic, converging contour with a parabolically decreasing corner radius. Data were obtained at several Mach numbers from static conditions to 1.2 for a range of nozzle pressure ratios and angles of attack. An oil paint flow visualization technique was used to qualitatively assess the effect of the convolutions. Results indicate that afterbody drag reduction by convoluted contouring is convolution location, Mach number, boattail angle, and NPR dependent. The forward convolution location was the most effective contouring geometry for drag reduction on the 22 afterbody, but was only effective for M < 0.95. At M = 0.8, drag was reduced 20 and 36 percent at NPRs of 5.4 and 7, respectively, but drag was increased 10 percent for M = 0.95 at NPR = 7. Convoluted contouring along the 15 boattail angle afterbody was not effective at reducing drag because the flow was minimally separated from the baseline afterbody, unlike the massive separation along the 22 boattail angle baseline afterbody.

  12. New quantum MDS-convolutional codes derived from constacyclic codes

    NASA Astrophysics Data System (ADS)

    Li, Fengwei; Yue, Qin

    2015-12-01

    In this paper, we utilize a family of Hermitian dual-containing constacyclic codes to construct classical and quantum MDS convolutional codes. Our classical and quantum convolutional codes are optimal in the sense that they attain the classical (quantum) generalized Singleton bound.

  13. (Ti, O)/Ti and (Ti, O, N)/Ti composite coatings fabricated via PIIID for the medical application of NiTi shape memory alloy.

    PubMed

    Sun, Tao; Wang, Lang-Ping; Wang, Min

    2011-02-01

    In this investigation, the plasma immersion ion implantation and deposition (PIIID) technique was used to fabricate (Ti, O)/Ti or (Ti, O, N)/Ti coatings on a NiTi shape memory alloy (SMA, 50.8 at.% Ni) to improve its corrosion, wear resistance, and bioactivity. After coating fabrication, the structure and properties of composite coatings were studied, and the coated and uncoated NiTi SMA samples were compared with each other. Scanning electron microscopic (SEM) examination of coating surfaces and cross-sections showed that (Ti, O)/Ti and (Ti, O, N)/Ti composite coatings were dense and uniform, having thickness values of 1.16 ± 0.08 μm and 0.95 ± 0.06 μm, respectively. X-ray diffraction (XRD) results revealed that there were no diffraction peaks corresponding to TiO(2) or TiN for (Ti, O)/Ti and (Ti, O, N)/Ti composite coatings, suggesting that after the PIIID treatment, TiO(2) and TiN were amorphous or nanosized in the coatings. Energy dispersive X-ray (EDX) analysis indicated that the interface between the coating and NiTi SMA substrate was gradual rather than sharp. In addition, EDX elemental mapping of coating cross-sections showed that Ni was depleted from the surface. Differential scanning calorimetry (DSC) curves revealed that the shape memory ability of NiTi SMA was not degraded by the PIIID treatment. The width of wear tracks on (Ti, O, N)/Ti coated NiTi SMA samples was reduced 6.5-fold, in comparison with that on uncoated samples. The corrosion potential (E(corr) ) was improved from -466.20 ± 37.82 mV for uncoated samples to 125.50 ± 21.49 mV and -185.40 ± 37.05 mV for (Ti, O)/Ti coated and (Ti, O, N)/Ti coated samples, respectively. Both types of coatings facilitated bone-like apatite formation on the surface of NiTi SMA in simulated body fluid (SBF), indicating their in vitro bioactivity.

  14. Robust smile detection using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Bianco, Simone; Celona, Luigi; Schettini, Raimondo

    2016-11-01

    We present a fully automated approach for smile detection. Faces are detected using a multiview face detector and aligned and scaled using automatically detected eye locations. Then, we use a convolutional neural network (CNN) to determine whether it is a smiling face or not. To this end, we investigate different shallow CNN architectures that can be trained even when the amount of learning data is limited. We evaluate our complete processing pipeline on the largest publicly available image database for smile detection in an uncontrolled scenario. We investigate the robustness of the method to different kinds of geometric transformations (rotation, translation, and scaling) due to imprecise face localization, and to several kinds of distortions (compression, noise, and blur). To the best of our knowledge, this is the first time that this type of investigation has been performed for smile detection. Experimental results show that our proposal outperforms state-of-the-art methods on both high- and low-quality images.

  15. Fully automated quantitative cephalometry using convolutional neural networks.

    PubMed

    Arık, Sercan Ö; Ibragimov, Bulat; Xing, Lei

    2017-01-01

    Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy ([Formula: see text] to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy ([Formula: see text] average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.

  16. A novel one-pot process for near-net-shape fabrication of open-porous resorbable hydroxyapatite/protein composites and in vivo assessment.

    PubMed

    Mueller, Berit; Koch, Dietmar; Lutz, Rainer; Schlegel, Karl A; Treccani, Laura; Rezwan, Kurosch

    2014-09-01

    We present a mild one-pot freeze gelation process for fabricating near-net, complex-shaped hydroxyapatite scaffolds and to directly incorporate active proteins during scaffold processing. In particular, the direct protein incorporation enables a simultaneous adjustment and control of scaffold microstructure, porosity, resorbability and enhancement of initial mechanical and handling stability. Two proteins, serum albumin and lysozyme, are selected and their effect on scaffold stability and microstructure investigated by biaxial strength tests, electron microscopy, and mercury intrusion porosimetry. The resulting hydroxyapatite/protein composites feature adjustable porosities from 50% to 70% and a mechanical strength ranging from 2 to 6 MPa comparable to that of human spongiosa without any sintering step. Scaffold degradation behaviour and protein release are assessed by in vitro studies. A preliminary in vivo assessment of scaffold biocompatibility and resorption behaviour in adult domestic pigs is discussed. After implantation, composites were resorbed up to 50% after only 4 weeks and up to 65% after 8 weeks. In addition, 14% new bone formation after 4 weeks and 37% after 8 weeks were detected. All these investigations demonstrate the outstanding suitability of the one-pot-process to create, in a customisable and reliable way, biocompatible scaffolds with sufficient mechanical strength for handling and surgical insertion, and for potential use as biodegradable bone substitutes and versatile platform for local drug delivery.

  17. Convolution modeling of two-domain, nonlinear water-level responses in karst aquifers (Invited)

    NASA Astrophysics Data System (ADS)

    Long, A. J.

    2009-12-01

    Convolution modeling is a useful method for simulating the hydraulic response of water levels to sinking streamflow or precipitation infiltration at the macro scale. This approach is particularly useful in karst aquifers, where the complex geometry of the conduit and pore network is not well characterized but can be represented approximately by a parametric impulse-response function (IRF) with very few parameters. For many applications, one-dimensional convolution models can be equally effective as complex two- or three-dimensional models for analyzing water-level responses to recharge. Moreover, convolution models are well suited for identifying and characterizing the distinct domains of quick flow and slow flow (e.g., conduit flow and diffuse flow). Two superposed lognormal functions were used in the IRF to approximate the impulses of the two flow domains. Nonlinear response characteristics of the flow domains were assessed by observing temporal changes in the IRFs. Precipitation infiltration was simulated by filtering the daily rainfall record with a backward-in-time exponential function that weights each day’s rainfall with the rainfall of previous days and thus accounts for the effects of soil moisture on aquifer infiltration. The model was applied to the Edwards aquifer in Texas and the Madison aquifer in South Dakota. Simulations of both aquifers showed similar characteristics, including a separation on the order of years between the quick-flow and slow-flow IRF peaks and temporal changes in the IRF shapes when water levels increased and empty pore spaces became saturated.

  18. Image quality of mixed convolution kernel in thoracic computed tomography.

    PubMed

    Neubauer, Jakob; Spira, Eva Maria; Strube, Juliane; Langer, Mathias; Voss, Christian; Kotter, Elmar

    2016-11-01

    The mixed convolution kernel alters his properties geographically according to the depicted organ structure, especially for the lung. Therefore, we compared the image quality of the mixed convolution kernel to standard soft and hard kernel reconstructions for different organ structures in thoracic computed tomography (CT) images.Our Ethics Committee approved this prospective study. In total, 31 patients who underwent contrast-enhanced thoracic CT studies were included after informed consent. Axial reconstructions were performed with hard, soft, and mixed convolution kernel. Three independent and blinded observers rated the image quality according to the European Guidelines for Quality Criteria of Thoracic CT for 13 organ structures. The observers rated the depiction of the structures in all reconstructions on a 5-point Likert scale. Statistical analysis was performed with the Friedman Test and post hoc analysis with the Wilcoxon rank-sum test.Compared to the soft convolution kernel, the mixed convolution kernel was rated with a higher image quality for lung parenchyma, segmental bronchi, and the border between the pleura and the thoracic wall (P < 0.03). Compared to the hard convolution kernel, the mixed convolution kernel was rated with a higher image quality for aorta, anterior mediastinal structures, paratracheal soft tissue, hilar lymph nodes, esophagus, pleuromediastinal border, large and medium sized pulmonary vessels and abdomen (P < 0.004) but a lower image quality for trachea, segmental bronchi, lung parenchyma, and skeleton (P < 0.001).The mixed convolution kernel cannot fully substitute the standard CT reconstructions. Hard and soft convolution kernel reconstructions still seem to be mandatory for thoracic CT.

  19. Space reactor shielding fabrication

    NASA Technical Reports Server (NTRS)

    Welch, F. H.

    1972-01-01

    The fabrication of space reactor neutron shielding by a melting and casting process utilizing lithium hydride is described. The first neutron shield fabricated is a large pancake shape 86 inches in diameter, containing about 1700 pounds of lithium hydride. This shield, fabricated by the unique melting and casting process, is the largest lithium hydride shield ever built.

  20. Development of an LSI maximum-likelihood convolutional decoder for advanced forward error correction capability on the NASA 30/20 GHz program

    NASA Technical Reports Server (NTRS)

    Clark, R. T.; Mccallister, R. D.

    1982-01-01

    The particular coding option identified as providing the best level of coding gain performance in an LSI-efficient implementation was the optimal constraint length five, rate one-half convolutional code. To determine the specific set of design parameters which optimally matches this decoder to the LSI constraints, a breadboard MCD (maximum-likelihood convolutional decoder) was fabricated and used to generate detailed performance trade-off data. The extensive performance testing data gathered during this design tradeoff study are summarized, and the functional and physical MCD chip characteristics are presented.

  1. Programmable convolution via the chirp Z-transform with CCD's

    NASA Technical Reports Server (NTRS)

    Buss, D. D.

    1977-01-01

    Technique filtering by convolution in frequency domain rather than in time domain presents possible solution to problem of programmable transversal filters. Process is accomplished through utilization of chip z-transform (CZT) with charge-coupled devices

  2. Metaheuristic Algorithms for Convolution Neural Network

    PubMed Central

    Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

  3. Accelerated unsteady flow line integral convolution.

    PubMed

    Liu, Zhanping; Moorhead, Robert J

    2005-01-01

    Unsteady flow line integral convolution (UFLIC) is a texture synthesis technique for visualizing unsteady flows with high temporal-spatial coherence. Unfortunately, UFLIC requires considerable time to generate each frame due to the huge amount of pathline integration that is computed for particle value scattering. This paper presents Accelerated UFLIC (AUFLIC) for near interactive (1 frame/second) visualization with 160,000 particles per frame. AUFLIC reuses pathlines in the value scattering process to reduce computationally expensive pathline integration. A flow-driven seeding strategy is employed to distribute seeds such that only a few of them need pathline integration while most seeds are placed along the pathlines advected at earlier times by other seeds upstream and, therefore, the known pathlines can be reused for fast value scattering. To maintain a dense scattering coverage to convey high temporal-spatial coherence while keeping the expense of pathline integration low, a dynamic seeding controller is designed to decide whether to advect, copy, or reuse a pathline. At a negligible memory cost, AUFLIC is 9 times faster than UFLIC with comparable image quality.

  4. Convolution kernels for multi-wavelength imaging

    NASA Astrophysics Data System (ADS)

    Boucaud, A.; Bocchio, M.; Abergel, A.; Orieux, F.; Dole, H.; Hadj-Youcef, M. A.

    2016-12-01

    Astrophysical images issued from different instruments and/or spectral bands often require to be processed together, either for fitting or comparison purposes. However each image is affected by an instrumental response, also known as point-spread function (PSF), that depends on the characteristics of the instrument as well as the wavelength and the observing strategy. Given the knowledge of the PSF in each band, a straightforward way of processing images is to homogenise them all to a target PSF using convolution kernels, so that they appear as if they had been acquired by the same instrument. We propose an algorithm that generates such PSF-matching kernels, based on Wiener filtering with a tunable regularisation parameter. This method ensures all anisotropic features in the PSFs to be taken into account. We compare our method to existing procedures using measured Herschel/PACS and SPIRE PSFs and simulated JWST/MIRI PSFs. Significant gains up to two orders of magnitude are obtained with respect to the use of kernels computed assuming Gaussian or circularised PSFs. A software to compute these kernels is available at https://github.com/aboucaud/pypher

  5. Event Discrimination using Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Menon, Hareesh; Hughes, Richard; Daling, Alec; Winer, Brian

    2017-01-01

    Convolutional Neural Networks (CNNs) are computational models that have been shown to be effective at classifying different types of images. We present a method to use CNNs to distinguish events involving the production of a top quark pair and a Higgs boson from events involving the production of a top quark pair and several quark and gluon jets. To do this, we generate and simulate data using MADGRAPH and DELPHES for a general purpose LHC detector at 13 TeV. We produce images using a particle flow algorithm by binning the particles geometrically based on their position in the detector and weighting the bins by the energy of each particle within each bin, and by defining channels based on particle types (charged track, neutral hadronic, neutral EM, lepton, heavy flavor). Our classification results are competitive with standard machine learning techniques. We have also looked into the classification of the substructure of the events, in a process known as scene labeling. In this context, we look for the presence of boosted objects (such as top quarks) with substructure encompassed within single jets. Preliminary results on substructure classification will be presented.

  6. Colonoscopic polyp detection using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Park, Sun Young; Sargent, Dusty

    2016-03-01

    Computer aided diagnosis (CAD) systems for medical image analysis rely on accurate and efficient feature extraction methods. Regardless of which type of classifier is used, the results will be limited if the input features are not diagnostically relevant and do not properly discriminate between the different classes of images. Thus, a large amount of research has been dedicated to creating feature sets that capture the salient features that physicians are able to observe in the images. Successful feature extraction reduces the semantic gap between the physician's interpretation and the computer representation of images, and helps to reduce the variability in diagnosis between physicians. Due to the complexity of many medical image classification tasks, feature extraction for each problem often requires domainspecific knowledge and a carefully constructed feature set for the specific type of images being classified. In this paper, we describe a method for automatic diagnostic feature extraction from colonoscopy images that may have general application and require a lower level of domain-specific knowledge. The work in this paper expands on our previous CAD algorithm for detecting polyps in colonoscopy video. In that work, we applied an eigenimage model to extract features representing polyps, normal tissue, diverticula, etc. from colonoscopy videos taken from various viewing angles and imaging conditions. Classification was performed using a conditional random field (CRF) model that accounted for the spatial and temporal adjacency relationships present in colonoscopy video. In this paper, we replace the eigenimage feature descriptor with features extracted from a convolutional neural network (CNN) trained to recognize the same image types in colonoscopy video. The CNN-derived features show greater invariance to viewing angles and image quality factors when compared to the eigenimage model. The CNN features are used as input to the CRF classifier as before. We report

  7. Inequalities and consequences of new convolutions for the fractional Fourier transform with Hermite weights

    NASA Astrophysics Data System (ADS)

    Anh, P. K.; Castro, L. P.; Thao, P. T.; Tuan, N. M.

    2017-01-01

    This paper presents new convolutions for the fractional Fourier transform which are somehow associated with the Hermite functions. Consequent inequalities and properties are derived for these convolutions, among which we emphasize two new types of Young's convolution inequalities. The results guarantee a general framework where the present convolutions are well-defined, allowing larger possibilities than the known ones for other convolutions. Furthermore, we exemplify the use of our convolutions by providing explicit solutions of some classes of integral equations which appear in engineering problems.

  8. Error-trellis syndrome decoding techniques for convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1985-01-01

    An error-trellis syndrome decoding technique for convolutional codes is developed. This algorithm is then applied to the entire class of systematic convolutional codes and to the high-rate, Wyner-Ash convolutional codes. A special example of the one-error-correcting Wyner-Ash code, a rate 3/4 code, is treated. The error-trellis syndrome decoding method applied to this example shows in detail how much more efficient syndrome decordig is than Viterbi decoding if applied to the same problem. For standard Viterbi decoding, 64 states are required, whereas in the example only 7 states are needed. Also, within the 7 states required for decoding, many fewer transitions are needed between the states.

  9. Error-trellis Syndrome Decoding Techniques for Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    An error-trellis syndrome decoding technique for convolutional codes is developed. This algorithm is then applied to the entire class of systematic convolutional codes and to the high-rate, Wyner-Ash convolutional codes. A special example of the one-error-correcting Wyner-Ash code, a rate 3/4 code, is treated. The error-trellis syndrome decoding method applied to this example shows in detail how much more efficient syndrome decoding is than Viterbi decoding if applied to the same problem. For standard Viterbi decoding, 64 states are required, whereas in the example only 7 states are needed. Also, within the 7 states required for decoding, many fewer transitions are needed between the states.

  10. Two dimensional convolute integers for machine vision and image recognition

    NASA Technical Reports Server (NTRS)

    Edwards, Thomas R.

    1988-01-01

    Machine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.

  11. Knowledge Based 3d Building Model Recognition Using Convolutional Neural Networks from LIDAR and Aerial Imageries

    NASA Astrophysics Data System (ADS)

    Alidoost, F.; Arefi, H.

    2016-06-01

    In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings' roofs automatically considering the complementary nature of height and RGB information.

  12. Mesh Convolutional Restricted Boltzmann Machines for Unsupervised Learning of Features With Structure Preservation on 3-D Meshes.

    PubMed

    Han, Zhizhong; Liu, Zhenbao; Han, Junwei; Vong, Chi-Man; Bu, Shuhui; Chen, Chun Long Philip

    2016-06-30

    Discriminative features of 3-D meshes are significant to many 3-D shape analysis tasks. However, handcrafted descriptors and traditional unsupervised 3-D feature learning methods suffer from several significant weaknesses: 1) the extensive human intervention is involved; 2) the local and global structure information of 3-D meshes cannot be preserved, which is in fact an important source of discriminability; 3) the irregular vertex topology and arbitrary resolution of 3-D meshes do not allow the direct application of the popular deep learning models; 4) the orientation is ambiguous on the mesh surface; and 5) the effect of rigid and nonrigid transformations on 3-D meshes cannot be eliminated. As a remedy, we propose a deep learning model with a novel irregular model structure, called mesh convolutional restricted Boltzmann machines (MCRBMs). MCRBM aims to simultaneously learn structure-preserving local and global features from a novel raw representation, local function energy distribution. In addition, multiple MCRBMs can be stacked into a deeper model, called mesh convolutional deep belief networks (MCDBNs). MCDBN employs a novel local structure preserving convolution (LSPC) strategy to convolve the geometry and the local structure learned by the lower MCRBM to the upper MCRBM. LSPC facilitates resolving the challenging issue of the orientation ambiguity on the mesh surface in MCDBN. Experiments using the proposed MCRBM and MCDBN were conducted on three common aspects: global shape retrieval, partial shape retrieval, and shape correspondence. Results show that the features learned by the proposed methods outperform the other state-of-the-art 3-D shape features.

  13. Bioprinting of 3D Convoluted Renal Proximal Tubules on Perfusable Chips

    PubMed Central

    Homan, Kimberly A.; Kolesky, David B.; Skylar-Scott, Mark A.; Herrmann, Jessica; Obuobi, Humphrey; Moisan, Annie; Lewis, Jennifer A.

    2016-01-01

    Three-dimensional models of kidney tissue that recapitulate human responses are needed for drug screening, disease modeling, and, ultimately, kidney organ engineering. Here, we report a bioprinting method for creating 3D human renal proximal tubules in vitro that are fully embedded within an extracellular matrix and housed in perfusable tissue chips, allowing them to be maintained for greater than two months. Their convoluted tubular architecture is circumscribed by proximal tubule epithelial cells and actively perfused through the open lumen. These engineered 3D proximal tubules on chip exhibit significantly enhanced epithelial morphology and functional properties relative to the same cells grown on 2D controls with or without perfusion. Upon introducing the nephrotoxin, Cyclosporine A, the epithelial barrier is disrupted in a dose-dependent manner. Our bioprinting method provides a new route for programmably fabricating advanced human kidney tissue models on demand. PMID:27725720

  14. Bioprinting of 3D Convoluted Renal Proximal Tubules on Perfusable Chips

    NASA Astrophysics Data System (ADS)

    Homan, Kimberly A.; Kolesky, David B.; Skylar-Scott, Mark A.; Herrmann, Jessica; Obuobi, Humphrey; Moisan, Annie; Lewis, Jennifer A.

    2016-10-01

    Three-dimensional models of kidney tissue that recapitulate human responses are needed for drug screening, disease modeling, and, ultimately, kidney organ engineering. Here, we report a bioprinting method for creating 3D human renal proximal tubules in vitro that are fully embedded within an extracellular matrix and housed in perfusable tissue chips, allowing them to be maintained for greater than two months. Their convoluted tubular architecture is circumscribed by proximal tubule epithelial cells and actively perfused through the open lumen. These engineered 3D proximal tubules on chip exhibit significantly enhanced epithelial morphology and functional properties relative to the same cells grown on 2D controls with or without perfusion. Upon introducing the nephrotoxin, Cyclosporine A, the epithelial barrier is disrupted in a dose-dependent manner. Our bioprinting method provides a new route for programmably fabricating advanced human kidney tissue models on demand.

  15. Voltage measurements at the vacuum post-hole convolute of the Z pulsed-power accelerator

    DOE PAGES

    Waisman, E. M.; McBride, R. D.; Cuneo, M. E.; ...

    2014-12-08

    Presented are voltage measurements taken near the load region on the Z pulsed-power accelerator using an inductive voltage monitor (IVM). Specifically, the IVM was connected to, and thus monitored the voltage at, the bottom level of the accelerator’s vacuum double post-hole convolute. Additional voltage and current measurements were taken at the accelerator’s vacuum-insulator stack (at a radius of 1.6 m) by using standard D-dot and B-dot probes, respectively. During postprocessing, the measurements taken at the stack were translated to the location of the IVM measurements by using a lossless propagation model of the Z accelerator’s magnetically insulated transmission lines (MITLs)more » and a lumped inductor model of the vacuum post-hole convolute. Across a wide variety of experiments conducted on the Z accelerator, the voltage histories obtained from the IVM and the lossless propagation technique agree well in overall shape and magnitude. However, large-amplitude, high-frequency oscillations are more pronounced in the IVM records. It is unclear whether these larger oscillations represent true voltage oscillations at the convolute or if they are due to noise pickup and/or transit-time effects and other resonant modes in the IVM. Results using a transit-time-correction technique and Fourier analysis support the latter. Regardless of which interpretation is correct, both true voltage oscillations and the excitement of resonant modes could be the result of transient electrical breakdowns in the post-hole convolute, though more information is required to determine definitively if such breakdowns occurred. Despite the larger oscillations in the IVM records, the general agreement found between the lossless propagation results and the results of the IVM shows that large voltages are transmitted efficiently through the MITLs on Z. These results are complementary to previous studies [R. D. McBride et al., Phys. Rev. ST Accel. Beams 13, 120401 (2010)] that showed

  16. Stacked Convolutional Denoising Auto-Encoders for Feature Representation.

    PubMed

    Du, Bo; Xiong, Wei; Wu, Jia; Zhang, Lefei; Zhang, Liangpei; Tao, Dacheng

    2016-03-16

    Deep networks have achieved excellent performance in learning representation from visual data. However, the supervised deep models like convolutional neural network require large quantities of labeled data, which are very expensive to obtain. To solve this problem, this paper proposes an unsupervised deep network, called the stacked convolutional denoising auto-encoders, which can map images to hierarchical representations without any label information. The network, optimized by layer-wise training, is constructed by stacking layers of denoising auto-encoders in a convolutional way. In each layer, high dimensional feature maps are generated by convolving features of the lower layer with kernels learned by a denoising auto-encoder. The auto-encoder is trained on patches extracted from feature maps in the lower layer to learn robust feature detectors. To better train the large network, a layer-wise whitening technique is introduced into the model. Before each convolutional layer, a whitening layer is embedded to sphere the input data. By layers of mapping, raw images are transformed into high-level feature representations which would boost the performance of the subsequent support vector machine classifier. The proposed algorithm is evaluated by extensive experimentations and demonstrates superior classification performance to state-of-the-art unsupervised networks.

  17. Segmenting delaminations in carbon fiber reinforced polymer composite CT using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Sammons, Daniel; Winfree, William P.; Burke, Eric; Ji, Shuiwang

    2016-02-01

    Nondestructive evaluation (NDE) utilizes a variety of techniques to inspect various materials for defects without causing changes to the material. X-ray computed tomography (CT) produces large volumes of three dimensional image data. Using the task of identifying delaminations in carbon fiber reinforced polymer (CFRP) composite CT, this work shows that it is possible to automate the analysis of these large volumes of CT data using a machine learning model known as a convolutional neural network (CNN). Further, tests on simulated data sets show that with a robust set of experimental data, it may be possible to go beyond just identification and instead accurately characterize the size and shape of the delaminations with CNNs.

  18. Two-dimensional convolute integers for analytical instrumentation

    NASA Technical Reports Server (NTRS)

    Edwards, T. R.

    1982-01-01

    As new analytical instruments and techniques emerge with increased dimensionality, a corresponding need is seen for data processing logic which can appropriately address the data. Two-dimensional measurements reveal enhanced unknown mixture analysis capability as a result of the greater spectral information content over two one-dimensional methods taken separately. It is noted that two-dimensional convolute integers are merely an extension of the work by Savitzky and Golay (1964). It is shown that these low-pass, high-pass and band-pass digital filters are truly two-dimensional and that they can be applied in a manner identical with their one-dimensional counterpart, that is, a weighted nearest-neighbor, moving average with zero phase shifting, convoluted integer (universal number) weighting coefficients.

  19. UFLIC: A Line Integral Convolution Algorithm for Visualizing Unsteady Flows

    NASA Technical Reports Server (NTRS)

    Shen, Han-Wei; Kao, David L.; Chancellor, Marisa K. (Technical Monitor)

    1997-01-01

    This paper presents an algorithm, UFLIC (Unsteady Flow LIC), to visualize vector data in unsteady flow fields. Using the Line Integral Convolution (LIC) as the underlying method, a new convolution algorithm is proposed that can effectively trace the flow's global features over time. The new algorithm consists of a time-accurate value depositing scheme and a successive feed-forward method. The value depositing scheme accurately models the flow advection, and the successive feed-forward method maintains the coherence between animation frames. Our new algorithm can produce time-accurate, highly coherent flow animations to highlight global features in unsteady flow fields. CFD scientists, for the first time, are able to visualize unsteady surface flows using our algorithm.

  20. Spectral density of generalized Wishart matrices and free multiplicative convolution.

    PubMed

    Młotkowski, Wojciech; Nowak, Maciej A; Penson, Karol A; Życzkowski, Karol

    2015-07-01

    We investigate the level density for several ensembles of positive random matrices of a Wishart-like structure, W=XX(†), where X stands for a non-Hermitian random matrix. In particular, making use of the Cauchy transform, we study the free multiplicative powers of the Marchenko-Pastur (MP) distribution, MP(⊠s), which for an integer s yield Fuss-Catalan distributions corresponding to a product of s-independent square random matrices, X=X(1)⋯X(s). New formulas for the level densities are derived for s=3 and s=1/3. Moreover, the level density corresponding to the generalized Bures distribution, given by the free convolution of arcsine and MP distributions, is obtained. We also explain the reason of such a curious convolution. The technique proposed here allows for the derivation of the level densities for several other cases.

  1. Spectral density of generalized Wishart matrices and free multiplicative convolution

    NASA Astrophysics Data System (ADS)

    Młotkowski, Wojciech; Nowak, Maciej A.; Penson, Karol A.; Życzkowski, Karol

    2015-07-01

    We investigate the level density for several ensembles of positive random matrices of a Wishart-like structure, W =X X† , where X stands for a non-Hermitian random matrix. In particular, making use of the Cauchy transform, we study the free multiplicative powers of the Marchenko-Pastur (MP) distribution, MP⊠s, which for an integer s yield Fuss-Catalan distributions corresponding to a product of s -independent square random matrices, X =X1⋯Xs . New formulas for the level densities are derived for s =3 and s =1 /3 . Moreover, the level density corresponding to the generalized Bures distribution, given by the free convolution of arcsine and MP distributions, is obtained. We also explain the reason of such a curious convolution. The technique proposed here allows for the derivation of the level densities for several other cases.

  2. Rationale-Augmented Convolutional Neural Networks for Text Classification

    PubMed Central

    Zhang, Ye; Marshall, Iain; Wallace, Byron C.

    2016-01-01

    We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions. PMID:28191551

  3. Self-Taught convolutional neural networks for short text clustering.

    PubMed

    Xu, Jiaming; Xu, Bo; Wang, Peng; Zheng, Suncong; Tian, Guanhua; Zhao, Jun; Xu, Bo

    2017-04-01

    Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC(2)), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text features are firstly embedded into compact binary codes by using one existing unsupervised dimensionality reduction method. Then, word embeddings are explored and fed into convolutional neural networks to learn deep feature representations, meanwhile the output units are used to fit the pre-trained binary codes in the training process. Finally, we get the optimal clusters by employing K-means to cluster the learned representations. Extensive experimental results demonstrate that the proposed framework is effective, flexible and outperform several popular clustering methods when tested on three public short text datasets.

  4. Deep learning for steganalysis via convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu

    2015-03-01

    Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.

  5. A new computational decoding complexity measure of convolutional codes

    NASA Astrophysics Data System (ADS)

    Benchimol, Isaac B.; Pimentel, Cecilio; Souza, Richard Demo; Uchôa-Filho, Bartolomeu F.

    2014-12-01

    This paper presents a computational complexity measure of convolutional codes well suitable for software implementations of the Viterbi algorithm (VA) operating with hard decision. We investigate the number of arithmetic operations performed by the decoding process over the conventional and minimal trellis modules. A relation between the complexity measure defined in this work and the one defined by McEliece and Lin is investigated. We also conduct a refined computer search for good convolutional codes (in terms of distance spectrum) with respect to two minimal trellis complexity measures. Finally, the computational cost of implementation of each arithmetic operation is determined in terms of machine cycles taken by its execution using a typical digital signal processor widely used for low-power telecommunications applications.

  6. New syndrome decoder for (n, 1) convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1983-01-01

    The letter presents a new syndrome decoding algorithm for the (n, 1) convolutional codes (CC) that is different and simpler than the previous syndrome decoding algorithm of Schalkwijk and Vinck. The new technique uses the general solution of the polynomial linear Diophantine equation for the error polynomial vector E(D). A recursive, Viterbi-like, algorithm is developed to find the minimum weight error vector E(D). An example is given for the binary nonsystematic (2, 1) CC.

  7. Fast convolution quadrature for the wave equation in three dimensions

    NASA Astrophysics Data System (ADS)

    Banjai, L.; Kachanovska, M.

    2014-12-01

    This work addresses the numerical solution of time-domain boundary integral equations arising from acoustic and electromagnetic scattering in three dimensions. The semidiscretization of the time-domain boundary integral equations by Runge-Kutta convolution quadrature leads to a lower triangular Toeplitz system of size N. This system can be solved recursively in an almost linear time (O(Nlog2⁡N)), but requires the construction of O(N) dense spatial discretizations of the single layer boundary operator for the Helmholtz equation. This work introduces an improvement of this algorithm that allows to solve the scattering problem in an almost linear time. The new approach is based on two main ingredients: the near-field reuse and the application of data-sparse techniques. Exponential decay of Runge-Kutta convolution weights wnh(d) outside of a neighborhood of d≈nh (where h is a time step) allows to avoid constructing the near-field (i.e. singular and near-singular integrals) for most of the discretizations of the single layer boundary operators (near-field reuse). The far-field of these matrices is compressed with the help of data-sparse techniques, namely, H-matrices and the high-frequency fast multipole method. Numerical experiments indicate the efficiency of the proposed approach compared to the conventional Runge-Kutta convolution quadrature algorithm.

  8. A model of traffic signs recognition with convolutional neural network

    NASA Astrophysics Data System (ADS)

    Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

    2016-10-01

    In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

  9. Fine-grained representation learning in convolutional autoencoders

    NASA Astrophysics Data System (ADS)

    Luo, Chang; Wang, Jie

    2016-03-01

    Convolutional autoencoders (CAEs) have been widely used as unsupervised feature extractors for high-resolution images. As a key component in CAEs, pooling is a biologically inspired operation to achieve scale and shift invariances, and the pooled representation directly affects the CAEs' performance. Fine-grained pooling, which uses small and dense pooling regions, encodes fine-grained visual cues and enhances local characteristics. However, it tends to be sensitive to spatial rearrangements. In most previous works, pooled features were obtained by empirically modulating parameters in CAEs. We see the CAE as a whole and propose a fine-grained representation learning law to extract better fine-grained features. This representation learning law suggests two directions for improvement. First, we probabilistically evaluate the discrimination-invariance tradeoff with fine-grained granularity in the pooled feature maps, and suggest the proper filter scale in the convolutional layer and appropriate whitening parameters in preprocessing step. Second, pooling approaches are combined with the sparsity degree in pooling regions, and we propose the preferable pooling approach. Experimental results on two independent benchmark datasets demonstrate that our representation learning law could guide CAEs to extract better fine-grained features and performs better in multiclass classification task. This paper also provides guidance for selecting appropriate parameters to obtain better fine-grained representation in other convolutional neural networks.

  10. Automatic localization of vertebrae based on convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Shen, Wei; Yang, Feng; Mu, Wei; Yang, Caiyun; Yang, Xin; Tian, Jie

    2015-03-01

    Localization of the vertebrae is of importance in many medical applications. For example, the vertebrae can serve as the landmarks in image registration. They can also provide a reference coordinate system to facilitate the localization of other organs in the chest. In this paper, we propose a new vertebrae localization method using convolutional neural networks (CNN). The main advantage of the proposed method is the removal of hand-crafted features. We construct two training sets to train two CNNs that share the same architecture. One is used to distinguish the vertebrae from other tissues in the chest, and the other is aimed at detecting the centers of the vertebrae. The architecture contains two convolutional layers, both of which are followed by a max-pooling layer. Then the output feature vector from the maxpooling layer is fed into a multilayer perceptron (MLP) classifier which has one hidden layer. Experiments were performed on ten chest CT images. We used leave-one-out strategy to train and test the proposed method. Quantitative comparison between the predict centers and ground truth shows that our convolutional neural networks can achieve promising localization accuracy without hand-crafted features.

  11. Calcium transport in the rabbit superficial proximal convoluted tubule

    SciTech Connect

    Ng, R.C.; Rouse, D.; Suki, W.N.

    1984-09-01

    Calcium transport was studied in isolated S2 segments of rabbit superficial proximal convoluted tubules. 45Ca was added to the perfusate for measurement of lumen-to-bath flux (JlbCa), to the bath for bath-to-lumen flux (JblCa), and to both perfusate and bath for net flux (JnetCa). In these studies, the perfusate consisted of an equilibrium solution that was designed to minimize water flux or electrochemical potential differences (PD). Under these conditions, JlbCa (9.1 +/- 1.0 peq/mm X min) was not different from JblCa (7.3 +/- 1.3 peq/mm X min), and JnetCa was not different from zero, which suggests that calcium transport in the superficial proximal convoluted tubule is due primarily to passive transport. The efflux coefficient was 9.5 +/- 1.2 X 10(-5) cm/s, which was not significantly different from the influx coefficient, 7.0 +/- 1.3 X 10(-5) cm/s. When the PD was made positive or negative with use of different perfusates, net calcium absorption or secretion was demonstrated, respectively, which supports a major role for passive transport. These results indicate that in the superficial proximal convoluted tubule of the rabbit, passive driving forces are the major determinants of calcium transport.

  12. Fabrication of a Kevlar liner assembly

    SciTech Connect

    Schloman, A.H.

    1980-07-01

    Several liner assemblies were fabricated with Kevlar 49 and epoxy using various wet layup and prepreg processes. A production process, using prepreg material, was developed for fabricating the liner and a wet layup molding process was used to fabricate the Kevlar hat-shaped tunnels. Fabrication of the tunnels using Kevlar prepreg with an autoclave curving process was evaluated.

  13. Super-resolution reconstruction algorithm based on adaptive convolution kernel size selection

    NASA Astrophysics Data System (ADS)

    Gao, Hang; Chen, Qian; Sui, Xiubao; Zeng, Junjie; Zhao, Yao

    2016-09-01

    Restricted by the detector technology and optical diffraction limit, the spatial resolution of infrared imaging system is difficult to achieve significant improvement. Super-Resolution (SR) reconstruction algorithm is an effective way to solve this problem. Among them, the SR algorithm based on multichannel blind deconvolution (MBD) estimates the convolution kernel only by low resolution observation images, according to the appropriate regularization constraints introduced by a priori assumption, to realize the high resolution image restoration. The algorithm has been shown effective when each channel is prime. In this paper, we use the significant edges to estimate the convolution kernel and introduce an adaptive convolution kernel size selection mechanism, according to the uncertainty of the convolution kernel size in MBD processing. To reduce the interference of noise, we amend the convolution kernel in an iterative process, and finally restore a clear image. Experimental results show that the algorithm can meet the convergence requirement of the convolution kernel estimation.

  14. Operational and convolution properties of three-dimensional Fourier transforms in spherical polar coordinates.

    PubMed

    Baddour, Natalie

    2010-10-01

    For functions that are best described with spherical coordinates, the three-dimensional Fourier transform can be written in spherical coordinates as a combination of spherical Hankel transforms and spherical harmonic series. However, to be as useful as its Cartesian counterpart, a spherical version of the Fourier operational toolset is required for the standard operations of shift, multiplication, convolution, etc. This paper derives the spherical version of the standard Fourier operation toolset. In particular, convolution in various forms is discussed in detail as this has important consequences for filtering. It is shown that standard multiplication and convolution rules do apply as long as the correct definition of convolution is applied.

  15. Design and fabrication of a bending rotation fatigue test rig for in situ electrochemical analysis during fatigue testing of NiTi shape memory alloy wires.

    PubMed

    Neelakantan, Lakshman; Zglinski, Jenni Kristin; Frotscher, Matthias; Eggeler, Gunther

    2013-03-01

    The current investigation proposes a novel method for simultaneous assessment of the electrochemical and structural fatigue properties of nickel-titanium shape memory alloy (NiTi SMA) wires. The design and layout of an in situ electrochemical cell in a custom-made bending rotation fatigue (BRF) test rig is presented. This newly designed test rig allows performing a wide spectrum of experiments for studying the influence of fatigue on corrosion and vice versa. This can be achieved by performing ex situ and∕or in situ measurements. The versatility of the combined electrochemical∕mechanical test rig is demonstrated by studying the electrochemical behavior of NiTi SMA wires in 0.9% NaCl electrolyte under load. The ex situ measurements allow addressing various issues, for example, the influence of pre-fatigue on the localized corrosion resistance, or the influence of hydrogen on fatigue life. Ex situ experiments showed that a pre-fatigued wire is more susceptible to localized corrosion. The synergetic effect can be concluded from the polarization studies and specifically from an in situ study of the open circuit potential (OCP) transients, which sensitively react to the elementary repassivation events related to the local failure of the oxide layer. It can also be used as an indicator for identifying the onset of the fatigue failure.

  16. Fabrication of 3D lawn-shaped N-doped porous carbon matrix/polyaniline nanocomposite as the electrode material for supercapacitors

    NASA Astrophysics Data System (ADS)

    Zhang, Xiuling; Ma, Li; Gan, Mengyu; Fu, Gang; Jin, Meng; Lei, Yao; Yang, Peishu; Yan, Maofa

    2017-02-01

    A facile approach to acquire electrode materials with prominent electrochemical property is pivotal to the progress of supercapacitors. 3D nitrogen-doped porous carbon matrix (PCM), with high specific surface area (SSA) up to 2720 m2 g-1, was obtained from the carbonization and activation of the nitrogen-enriched composite precursor (graphene/polyaniline). Then 3D lawn-shaped PCM/PANI composite was obtained by the simple in-situ polymerization. The morphology and structure of these resulting composites were characterized by combining SEM and TEM measurements, Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD) spectroscopy analyses and Raman spectroscope. The element content of all samples was evaluated using CHN analysis. The results of electrochemical testing indicated that the PCM/PANI composite displays a higher capacitance value of 527 F g-1 at 1 A g-1 compared to 338 F g-1 for pure PANI, and exhibits appreciable rate capability with a retention of 76% at 20 A g-1 as well as fine long-term cycling performance (with 88% retention of specific capacitance after 1000 cycles at 10 A g-1). Simultaneously, the excellent capacitance performance coupled with the facile synthesis of PCM/PANI indicates it is a promising electrode material for supercapacitors.

  17. Design and fabrication of a bending rotation fatigue test rig for in situ electrochemical analysis during fatigue testing of NiTi shape memory alloy wires

    SciTech Connect

    Neelakantan, Lakshman; Zglinski, Jenni Kristin; Eggeler, Gunther; Frotscher, Matthias

    2013-03-15

    The current investigation proposes a novel method for simultaneous assessment of the electrochemical and structural fatigue properties of nickel-titanium shape memory alloy (NiTi SMA) wires. The design and layout of an in situ electrochemical cell in a custom-made bending rotation fatigue (BRF) test rig is presented. This newly designed test rig allows performing a wide spectrum of experiments for studying the influence of fatigue on corrosion and vice versa. This can be achieved by performing ex situ and/or in situ measurements. The versatility of the combined electrochemical/mechanical test rig is demonstrated by studying the electrochemical behavior of NiTi SMA wires in 0.9% NaCl electrolyte under load. The ex situ measurements allow addressing various issues, for example, the influence of pre-fatigue on the localized corrosion resistance, or the influence of hydrogen on fatigue life. Ex situ experiments showed that a pre-fatigued wire is more susceptible to localized corrosion. The synergetic effect can be concluded from the polarization studies and specifically from an in situ study of the open circuit potential (OCP) transients, which sensitively react to the elementary repassivation events related to the local failure of the oxide layer. It can also be used as an indicator for identifying the onset of the fatigue failure.

  18. Convolution Algebra for Fluid Modes with Finite Energy

    DTIC Science & Technology

    1992-04-01

    PHILLIPS LABORATORY AIR FORCE SYSTEMS COMMAND UNITED STATES AIR FORCE HANSCOM AIR FORCE BASE, MASSACHIUSETTS 01731-5000 94-22604 "This technical report ’-as...with finite spatial and temporal extents. At Boston University, we have developed a full form of wavelet expansion which has the advantage over more...distribution: 00 bX =00 0l if, TZ< VPf (X) = V •a,,,’(x) = E bnb 𔄀(x) where b, =otherwise (34) V=o ,i=o a._, otherwise 7 The convolution of two

  19. Visualizing Vector Fields Using Line Integral Convolution and Dye Advection

    NASA Technical Reports Server (NTRS)

    Shen, Han-Wei; Johnson, Christopher R.; Ma, Kwan-Liu

    1996-01-01

    We present local and global techniques to visualize three-dimensional vector field data. Using the Line Integral Convolution (LIC) method to image the global vector field, our new algorithm allows the user to introduce colored 'dye' into the vector field to highlight local flow features. A fast algorithm is proposed that quickly recomputes the dyed LIC images. In addition, we introduce volume rendering methods that can map the LIC texture on any contour surface and/or translucent region defined by additional scalar quantities, and can follow the advection of colored dye throughout the volume.

  20. Surrogacy theory and models of convoluted organic systems.

    PubMed

    Konopka, Andrzej K

    2007-03-01

    The theory of surrogacy is briefly outlined as one of the conceptual foundations of systems biology that has been developed for the last 30 years in the context of Hertz-Rosen modeling relationship. Conceptual foundations of modeling convoluted (biologically complex) systems are briefly reviewed and discussed in terms of current and future research in systems biology. New as well as older results that pertain to the concepts of modeling relationship, sequence of surrogacies, cascade of representations, complementarity, analogy, metaphor, and epistemic time are presented together with a classification of models in a cascade. Examples of anticipated future applications of surrogacy theory in life sciences are briefly discussed.

  1. Medical image fusion using the convolution of Meridian distributions.

    PubMed

    Agrawal, Mayank; Tsakalides, Panagiotis; Achim, Alin

    2010-01-01

    The aim of this paper is to introduce a novel non-Gaussian statistical model-based approach for medical image fusion based on the Meridian distribution. The paper also includes a new approach to estimate the parameters of generalized Cauchy distribution. The input images are first decomposed using the Dual-Tree Complex Wavelet Transform (DT-CWT) with the subband coefficients modelled as Meridian random variables. Then, the convolution of Meridian distributions is applied as a probabilistic prior to model the fused coefficients, and the weights used to combine the source images are optimised via Maximum Likelihood (ML) estimation. The superior performance of the proposed method is demonstrated using medical images.

  2. Convolution seal for transition duct in turbine system

    DOEpatents

    Flanagan, James Scott; LeBegue, Jeffrey Scott; McMahan, Kevin Weston; Dillard, Daniel Jackson; Pentecost, Ronnie Ray

    2015-05-26

    A turbine system is disclosed. In one embodiment, the turbine system includes a transition duct. The transition duct includes an inlet, an outlet, and a passage extending between the inlet and the outlet and defining a longitudinal axis, a radial axis, and a tangential axis. The outlet of the transition duct is offset from the inlet along the longitudinal axis and the tangential axis. The transition duct further includes an interface feature for interfacing with an adjacent transition duct. The turbine system further includes a convolution seal contacting the interface feature to provide a seal between the interface feature and the adjacent transition duct.

  3. Simplified Syndrome Decoding of (n, 1) Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1983-01-01

    A new syndrome decoding algorithm for the (n, 1) convolutional codes (CC) that is different and simpler than the previous syndrome decoding algorithm of Schalkwijk and Vinck is presented. The new algorithm uses the general solution of the polynomial linear Diophantine equation for the error polynomial vector E(D). This set of Diophantine solutions is a coset of the CC space. A recursive or Viterbi-like algorithm is developed to find the minimum weight error vector cirumflex E(D) in this error coset. An example illustrating the new decoding algorithm is given for the binary nonsymmetric (2,1)CC.

  4. New Syndrome Decoding Techniques for the (n, K) Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1983-01-01

    This paper presents a new syndrome decoding algorithm for the (n,k) convolutional codes (CC) which differs completely from an earlier syndrome decoding algorithm of Schalkwijk and Vinck. The new algorithm is based on the general solution of the syndrome equation, a linear Diophantine equation for the error polynomial vector E(D). The set of Diophantine solutions is a coset of the CC. In this error coset a recursive, Viterbi-like algorithm is developed to find the minimum weight error vector (circumflex)E(D). An example, illustrating the new decoding algorithm, is given for the binary nonsystemmatic (3,1)CC.

  5. New syndrome decoding techniques for the (n, k) convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Truong, T. K.

    1984-01-01

    This paper presents a new syndrome decoding algorithm for the (n, k) convolutional codes (CC) which differs completely from an earlier syndrome decoding algorithm of Schalkwijk and Vinck. The new algorithm is based on the general solution of the syndrome equation, a linear Diophantine equation for the error polynomial vector E(D). The set of Diophantine solutions is a coset of the CC. In this error coset a recursive, Viterbi-like algorithm is developed to find the minimum weight error vector (circumflex)E(D). An example, illustrating the new decoding algorithm, is given for the binary nonsystemmatic (3, 1)CC. Previously announced in STAR as N83-34964

  6. Continuous speech recognition based on convolutional neural network

    NASA Astrophysics Data System (ADS)

    Zhang, Qing-qing; Liu, Yong; Pan, Jie-lin; Yan, Yong-hong

    2015-07-01

    Convolutional Neural Networks (CNNs), which showed success in achieving translation invariance for many image processing tasks, are investigated for continuous speech recognitions in the paper. Compared to Deep Neural Networks (DNNs), which have been proven to be successful in many speech recognition tasks nowadays, CNNs can reduce the NN model sizes significantly, and at the same time achieve even better recognition accuracies. Experiments on standard speech corpus TIMIT showed that CNNs outperformed DNNs in the term of the accuracy when CNNs had even smaller model size.

  7. Convolution seal for transition duct in turbine system

    DOEpatents

    Flanagan, James Scott; LeBegue, Jeffrey Scott; McMahan, Kevin Weston; Dillard, Daniel Jackson; Pentecost, Ronnie Ray

    2015-03-10

    A turbine system is disclosed. In one embodiment, the turbine system includes a transition duct. The transition duct includes an inlet, an outlet, and a passage extending between the inlet and the outlet and defining a longitudinal axis, a radial axis, and a tangential axis. The outlet of the transition duct is offset from the inlet along the longitudinal axis and the tangential axis. The transition duct further includes an interface member for interfacing with a turbine section. The turbine system further includes a convolution seal contacting the interface member to provide a seal between the interface member and the turbine section.

  8. Convolutional neural networks for synthetic aperture radar classification

    NASA Astrophysics Data System (ADS)

    Profeta, Andrew; Rodriguez, Andres; Clouse, H. Scott

    2016-05-01

    For electro-optical object recognition, convolutional neural networks (CNNs) are the state-of-the-art. For large datasets, CNNs are able to learn meaningful features used for classification. However, their application to synthetic aperture radar (SAR) has been limited. In this work we experimented with various CNN architectures on the MSTAR SAR dataset. As the input to the CNN we used the magnitude and phase (2 channels) of the SAR imagery. We used the deep learning toolboxes CAFFE and Torch7. Our results show that we can achieve 93% accuracy on the MSTAR dataset using CNNs.

  9. Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data

    NASA Astrophysics Data System (ADS)

    Anirudh, Rushil; Thiagarajan, Jayaraman J.; Bremer, Timo; Kim, Hyojin

    2016-03-01

    Early detection of lung nodules is currently the one of the most effective ways to predict and treat lung cancer. As a result, the past decade has seen a lot of focus on computer aided diagnosis (CAD) of lung nodules, whose goal is to efficiently detect, segment lung nodules and classify them as being benign or malignant. Effective detection of such nodules remains a challenge due to their arbitrariness in shape, size and texture. In this paper, we propose to employ 3D convolutional neural networks (CNN) to learn highly discriminative features for nodule detection in lieu of hand-engineered ones such as geometric shape or texture. While 3D CNNs are promising tools to model the spatio-temporal statistics of data, they are limited by their need for detailed 3D labels, which can be prohibitively expensive when compared obtaining 2D labels. Existing CAD methods rely on obtaining detailed labels for lung nodules, to train models, which is also unrealistic and time consuming. To alleviate this challenge, we propose a solution wherein the expert needs to provide only a point label, i.e., the central pixel of of the nodule, and its largest expected size. We use unsupervised segmentation to grow out a 3D region, which is used to train the CNN. Using experiments on the SPIE-LUNGx dataset, we show that the network trained using these weak labels can produce reasonably low false positive rates with a high sensitivity, even in the absence of accurate 3D labels.

  10. Convolutional neural network features based change detection in satellite images

    NASA Astrophysics Data System (ADS)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  11. Convolutional neural network architectures for predicting DNA–protein binding

    PubMed Central

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  12. Multichannel Convolutional Neural Network for Biological Relation Extraction

    PubMed Central

    Quan, Chanqin; Sun, Xiao; Bai, Wenjun

    2016-01-01

    The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of “vocabulary gap” and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores. PMID:28053977

  13. Deep Convolutional Neural Networks for large-scale speech tasks.

    PubMed

    Sainath, Tara N; Kingsbury, Brian; Saon, George; Soltau, Hagen; Mohamed, Abdel-rahman; Dahl, George; Ramabhadran, Bhuvana

    2015-04-01

    Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, we hypothesize that CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary continuous speech recognition (LVCSR) tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is an appropriate number of hidden units, what is the best pooling strategy. Second, investigate how to incorporate speaker-adapted features, which cannot directly be modeled by CNNs as they do not obey locality in frequency, into the CNN framework. Third, given the importance of sequence training for speech tasks, we introduce a strategy to use ReLU+dropout during Hessian-free sequence training of CNNs. Experiments on 3 LVCSR tasks indicate that a CNN with the proposed speaker-adapted and ReLU+dropout ideas allow for a 12%-14% relative improvement in WER over a strong DNN system, achieving state-of-the art results in these 3 tasks.

  14. A Mathematical Motivation for Complex-Valued Convolutional Networks.

    PubMed

    Tygert, Mark; Bruna, Joan; Chintala, Soumith; LeCun, Yann; Piantino, Serkan; Szlam, Arthur

    2016-05-01

    A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with complex-valued vectors, followed by (2) taking the absolute value of every entry of the resulting vectors, followed by (3) local averaging. For processing real-valued random vectors, complex-valued convnets can be viewed as data-driven multiscale windowed power spectra, data-driven multiscale windowed absolute spectra, data-driven multiwavelet absolute values, or (in their most general configuration) data-driven nonlinear multiwavelet packets. Indeed, complex-valued convnets can calculate multiscale windowed spectra when the convnet filters are windowed complex-valued exponentials. Standard real-valued convnets, using rectified linear units (ReLUs), sigmoidal (e.g., logistic or tanh) nonlinearities, or max pooling, for example, do not obviously exhibit the same exact correspondence with data-driven wavelets (whereas for complex-valued convnets, the correspondence is much more than just a vague analogy). Courtesy of the exact correspondence, the remarkably rich and rigorous body of mathematical analysis for wavelets applies directly to (complex-valued) convnets.

  15. Fluence-convolution broad-beam (FCBB) dose calculation.

    PubMed

    Lu, Weiguo; Chen, Mingli

    2010-12-07

    IMRT optimization requires a fast yet relatively accurate algorithm to calculate the iteration dose with small memory demand. In this paper, we present a dose calculation algorithm that approaches these goals. By decomposing the infinitesimal pencil beam (IPB) kernel into the central axis (CAX) component and lateral spread function (LSF) and taking the beam's eye view (BEV), we established a non-voxel and non-beamlet-based dose calculation formula. Both LSF and CAX are determined by a commissioning procedure using the collapsed-cone convolution/superposition (CCCS) method as the standard dose engine. The proposed dose calculation involves a 2D convolution of a fluence map with LSF followed by ray tracing based on the CAX lookup table with radiological distance and divergence correction, resulting in complexity of O(N(3)) both spatially and temporally. This simple algorithm is orders of magnitude faster than the CCCS method. Without pre-calculation of beamlets, its implementation is also orders of magnitude smaller than the conventional voxel-based beamlet-superposition (VBS) approach. We compared the presented algorithm with the CCCS method using simulated and clinical cases. The agreement was generally within 3% for a homogeneous phantom and 5% for heterogeneous and clinical cases. Combined with the 'adaptive full dose correction', the algorithm is well suitable for calculating the iteration dose during IMRT optimization.

  16. Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.

    PubMed

    Dürr, Oliver; Sick, Beate

    2016-10-01

    Deep learning methods are currently outperforming traditional state-of-the-art computer vision algorithms in diverse applications and recently even surpassed human performance in object recognition. Here we demonstrate the potential of deep learning methods to high-content screening-based phenotype classification. We trained a deep learning classifier in the form of convolutional neural networks with approximately 40,000 publicly available single-cell images from samples treated with compounds from four classes known to lead to different phenotypes. The input data consisted of multichannel images. The construction of appropriate feature definitions was part of the training and carried out by the convolutional network, without the need for expert knowledge or handcrafted features. We compare our results against the recent state-of-the-art pipeline in which predefined features are extracted from each cell using specialized software and then fed into various machine learning algorithms (support vector machine, Fisher linear discriminant, random forest) for classification. The performance of all classification approaches is evaluated on an untouched test image set with known phenotype classes. Compared to the best reference machine learning algorithm, the misclassification rate is reduced from 8.9% to 6.6%.

  17. Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval.

    PubMed

    Wei, Xiu-Shen; Luo, Jian-Hao; Wu, Jianxin; Zhou, Zhi-Hua

    2017-03-27

    Deep convolutional neural network models pretrained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, let alone the unsupervised retrieval task. We propose the Selective Convolutional Descriptor Aggregation (SCDA) method. SCDA firstly localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and dimensionality reduced into a short feature vector using the best practices we found. SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained datasets confirm the effectiveness of SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high mean average precision in fine-grained retrieval. Moreover, on general image retrieval datasets, SCDA achieves comparable retrieval results with state-of-the-art general image retrieval approaches.

  18. Enhancing Neutron Beam Production with a Convoluted Moderator

    SciTech Connect

    Iverson, Erik B; Baxter, David V; Muhrer, Guenter; Ansell, Stuart; Gallmeier, Franz X; Dalgliesh, Robert; Lu, Wei; Kaiser, Helmut

    2014-10-01

    We describe a new concept for a neutron moderating assembly resulting in the more efficient production of slow neutron beams. The Convoluted Moderator, a heterogeneous stack of interleaved moderating material and nearly transparent single-crystal spacers, is a directionally-enhanced neutron beam source, improving beam effectiveness over an angular range comparable to the range accepted by neutron beam lines and guides. We have demonstrated gains of 50% in slow neutron intensity for a given fast neutron production rate while simultaneously reducing the wavelength-dependent emission time dispersion by 25%, both coming from a geometric effect in which the neutron beam lines view a large surface area of moderating material in a relatively small volume. Additionally, we have confirmed a Bragg-enhancement effect arising from coherent scattering within the single-crystal spacers. We have not observed hypothesized refractive effects leading to additional gains at long wavelength. In addition to confirmation of the validity of the Convoluted Moderator concept, our measurements provide a series of benchmark experiments suitable for developing simulation and analysis techniques for practical optimization and eventual implementation at slow neutron source facilities.

  19. Classifications of Multispectral Colorectal Cancer Tissues Using Convolution Neural Network

    PubMed Central

    Haj-Hassan, Hawraa; Chaddad, Ahmad; Harkouss, Youssef; Desrosiers, Christian; Toews, Matthew; Tanougast, Camel

    2017-01-01

    Background: Colorectal cancer (CRC) is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs) to predict three tissue types related to the progression of CRC: benign hyperplasia (BH), intraepithelial neoplasia (IN), and carcinoma (Ca). Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca). An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

  20. An optimal nonorthogonal separation of the anisotropic Gaussian convolution filter.

    PubMed

    Lampert, Christoph H; Wirjadi, Oliver

    2006-11-01

    We give an analytical and geometrical treatment of what it means to separate a Gaussian kernel along arbitrary axes in R(n), and we present a separation scheme that allows us to efficiently implement anisotropic Gaussian convolution filters for data of arbitrary dimensionality. Based on our previous analysis we show that this scheme is optimal with regard to the number of memory accesses and interpolation operations needed. The proposed method relies on nonorthogonal convolution axes and works completely in image space. Thus, it avoids the need for a fast Fourier transform (FFT)-subroutine. Depending on the accuracy and speed requirements, different interpolation schemes and methods to implement the one-dimensional Gaussian (finite impulse response and infinite impulse response) can be integrated. Special emphasis is put on analyzing the performance and accuracy of the new method. In particular, we show that without any special optimization of the source code, it can perform anisotropic Gaussian filtering faster than methods relying on the FFT.

  1. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    NASA Astrophysics Data System (ADS)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  2. Experimental study of current loss and plasma formation in the Z machine post-hole convolute

    NASA Astrophysics Data System (ADS)

    Gomez, M. R.; Gilgenbach, R. M.; Cuneo, M. E.; Jennings, C. A.; McBride, R. D.; Waisman, E. M.; Hutsel, B. T.; Stygar, W. A.; Rose, D. V.; Maron, Y.

    2017-01-01

    The Z pulsed-power generator at Sandia National Laboratories drives high energy density physics experiments with load currents of up to 26 MA. Z utilizes a double post-hole convolute to combine the current from four parallel magnetically insulated transmission lines into a single transmission line just upstream of the load. Current loss is observed in most experiments and is traditionally attributed to inefficient convolute performance. The apparent loss current varies substantially for z-pinch loads with different inductance histories; however, a similar convolute impedance history is observed for all load types. This paper details direct spectroscopic measurements of plasma density, temperature, and apparent and actual plasma closure velocities within the convolute. Spectral measurements indicate a correlation between impedance collapse and plasma formation in the convolute. Absorption features in the spectra show the convolute plasma consists primarily of hydrogen, which likely forms from desorbed electrode contaminant species such as H2O , H2 , and hydrocarbons. Plasma densities increase from 1 ×1016 cm-3 (level of detectability) just before peak current to over 1 ×1017 cm-3 at stagnation (tens of ns later). The density seems to be highest near the cathode surface, with an apparent cathode to anode plasma velocity in the range of 35 - 50 cm /μ s . Similar plasma conditions and convolute impedance histories are observed in experiments with high and low losses, suggesting that losses are driven largely by load dynamics, which determine the voltage on the convolute.

  3. There is no MacWilliams identity for convolutional codes. [transmission gain comparison

    NASA Technical Reports Server (NTRS)

    Shearer, J. B.; Mceliece, R. J.

    1977-01-01

    An example is provided of two convolutional codes that have the same transmission gain but whose dual codes do not. This shows that no analog of the MacWilliams identity for block codes can exist relating the transmission gains of a convolutional code and its dual.

  4. Using convolutional decoding to improve time delay and phase estimation in digital communications

    DOEpatents

    Ormesher, Richard C.; Mason, John J.

    2010-01-26

    The time delay and/or phase of a communication signal received by a digital communication receiver can be estimated based on a convolutional decoding operation that the communication receiver performs on the received communication signal. If the original transmitted communication signal has been spread according to a spreading operation, a corresponding despreading operation can be integrated into the convolutional decoding operation.

  5. The effect of whitening transformation on pooling operations in convolutional autoencoders

    NASA Astrophysics Data System (ADS)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua

    2015-12-01

    Convolutional autoencoders (CAEs) are unsupervised feature extractors for high-resolution images. In the pre-processing step, whitening transformation has widely been adopted to remove redundancy by making adjacent pixels less correlated. Pooling is a biologically inspired operation to reduce the resolution of feature maps and achieve spatial invariance in convolutional neural networks. Conventionally, pooling methods are mainly determined empirically in most previous work. Therefore, our main purpose is to study the relationship between whitening processing and pooling operations in convolutional autoencoders for image classification. We propose an adaptive pooling approach based on the concepts of information entropy to test the effect of whitening on pooling in different conditions. Experimental results on benchmark datasets indicate that the performance of pooling strategies is associated with the distribution of feature activations, which can be affected by whitening processing. This provides guidance for the selection of pooling methods in convolutional autoencoders and other convolutional neural networks.

  6. Shaping by Deposition

    DTIC Science & Technology

    1991-01-01

    pasticprototypegreater design flexibility , rapid fabrication, and reduce cost.odels are built directly fronm liquid photopolymers by Shaping deposition processes build...ate on design models to help the designer create manufactura - ble designs on the basis of requirements and limitations of the This paper describes the

  7. Low-dose CT via convolutional neural network

    PubMed Central

    Chen, Hu; Zhang, Yi; Zhang, Weihua; Liao, Peixi; Li, Ke; Zhou, Jiliu; Wang, Ge

    2017-01-01

    In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods. PMID:28270976

  8. Drug-Drug Interaction Extraction via Convolutional Neural Networks

    PubMed Central

    Liu, Shengyu; Tang, Buzhou; Chen, Qingcai; Wang, Xiaolong

    2016-01-01

    Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%. PMID:26941831

  9. Small convolution kernels for high-fidelity image restoration

    NASA Technical Reports Server (NTRS)

    Reichenbach, Stephen E.; Park, Stephen K.

    1991-01-01

    An algorithm is developed for computing the mean-square-optimal values for small, image-restoration kernels. The algorithm is based on a comprehensive, end-to-end imaging system model that accounts for the important components of the imaging process: the statistics of the scene, the point-spread function of the image-gathering device, sampling effects, noise, and display reconstruction. Subject to constraints on the spatial support of the kernel, the algorithm generates the kernel values that restore the image with maximum fidelity, that is, the kernel minimizes the expected mean-square restoration error. The algorithm is consistent with the derivation of the spatially unconstrained Wiener filter, but leads to a small, spatially constrained kernel that, unlike the unconstrained filter, can be efficiently implemented by convolution. Simulation experiments demonstrate that for a wide range of imaging systems these small kernels can restore images with fidelity comparable to images restored with the unconstrained Wiener filter.

  10. Convolution quadrature for the wave equation with impedance boundary conditions

    NASA Astrophysics Data System (ADS)

    Sauter, S. A.; Schanz, M.

    2017-04-01

    We consider the numerical solution of the wave equation with impedance boundary conditions and start from a boundary integral formulation for its discretization. We develop the generalized convolution quadrature (gCQ) to solve the arising acoustic retarded potential integral equation for this impedance problem. For the special case of scattering from a spherical object, we derive representations of analytic solutions which allow to investigate the effect of the impedance coefficient on the acoustic pressure analytically. We have performed systematic numerical experiments to study the convergence rates as well as the sensitivity of the acoustic pressure from the impedance coefficients. Finally, we apply this method to simulate the acoustic pressure in a building with a fairly complicated geometry and to study the influence of the impedance coefficient also in this situation.

  11. Discovering characteristic landmarks on ancient coins using convolutional networks

    NASA Astrophysics Data System (ADS)

    Kim, Jongpil; Pavlovic, Vladimir

    2017-01-01

    We propose a method to find characteristic landmarks and recognize ancient Roman imperial coins using deep convolutional neural networks (CNNs) combined with expert-designed domain hierarchies. We first propose a framework to recognize Roman coins that exploits the hierarchical knowledge structure embedded in the coin domain, which we combine with the CNN-based category classifiers. We next formulate an optimization problem to discover class-specific salient coin regions. Analysis of discovered salient regions confirms that they are largely consistent with human expert annotations. Experimental results show that the proposed framework is able to effectively recognize ancient Roman coins as well as successfully identify landmarks on the coins. For this research, we have collected a Roman coin dataset where all coins are annotated and consist of obverse (head) and reverse (tail) images.

  12. Tomography by iterative convolution - Empirical study and application to interferometry

    NASA Technical Reports Server (NTRS)

    Vest, C. M.; Prikryl, I.

    1984-01-01

    An algorithm for computer tomography has been developed that is applicable to reconstruction from data having incomplete projections because an opaque object blocks some of the probing radiation as it passes through the object field. The algorithm is based on iteration between the object domain and the projection (Radon transform) domain. Reconstructions are computed during each iteration by the well-known convolution method. Although it is demonstrated that this algorithm does not converge, an empirically justified criterion for terminating the iteration when the most accurate estimate has been computed is presented. The algorithm has been studied by using it to reconstruct several different object fields with several different opaque regions. It also has been used to reconstruct aerodynamic density fields from interferometric data recorded in wind tunnel tests.

  13. Finding the complete path and weight enumerators of convolutional codes

    NASA Technical Reports Server (NTRS)

    Onyszchuk, I.

    1990-01-01

    A method for obtaining the complete path enumerator T(D, L, I) of a convolutional code is described. A system of algebraic equations is solved, using a new algorithm for computing determinants, to obtain T(D, L, I) for the (7,1/2) NASA standard code. Generating functions, derived from T(D, L, I) are used to upper bound Viterbi decoder error rates. This technique is currently feasible for constraint length K less than 10 codes. A practical, fast algorithm is presented for computing the leading nonzero coefficients of the generating functions used to bound the performance of constraint length K less than 20 codes. Code profiles with about 50 nonzero coefficients are obtained with this algorithm for the experimental K = 15, rate 1/4, code in the Galileo mission and for the proposed K = 15, rate 1/6, 2-dB code.

  14. Enhanced Line Integral Convolution with Flow Feature Detection

    NASA Technical Reports Server (NTRS)

    Lane, David; Okada, Arthur

    1996-01-01

    The Line Integral Convolution (LIC) method, which blurs white noise textures along a vector field, is an effective way to visualize overall flow patterns in a 2D domain. The method produces a flow texture image based on the input velocity field defined in the domain. Because of the nature of the algorithm, the texture image tends to be blurry. This sometimes makes it difficult to identify boundaries where flow separation and reattachments occur. We present techniques to enhance LIC texture images and use colored texture images to highlight flow separation and reattachment boundaries. Our techniques have been applied to several flow fields defined in 3D curvilinear multi-block grids and scientists have found the results to be very useful.

  15. Learning to Generate Chairs, Tables and Cars with Convolutional Networks.

    PubMed

    Dosovitskiy, Alexey; Springenberg, Jost; Tatarchenko, Maxim; Brox, Thomas

    2016-05-12

    We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color. We train the networks on rendered 3D models of chairs, tables, and cars. Our experiments show that the networks do not merely learn all images by heart, but rather find a meaningful representation of 3D models allowing them to assess the similarity of different models, interpolate between given views to generate the missing ones, extrapolate views, and invent new objects not present in the training set by recombining training instances, or even two different object classes. Moreover, we show that such generative networks can be used to find correspondences between different objects from the dataset, outperforming existing approaches on this task.

  16. Modifying real convolutional codes for protecting digital filtering systems

    NASA Technical Reports Server (NTRS)

    Redinbo, G. R.; Zagar, Bernhard

    1993-01-01

    A novel method is proposed for protecting digital filters from temporary and permanent failures that are not easily detected by conventional fault-tolerant computer design principles, on the basis of the error-detecting properties of real convolutional codes. Erroneous behavior is detected by externally comparing the calculated and regenerated parity samples. Great simplifications are obtainable by modifying the code structure to yield simplified parity channels with finite impulse response structures. A matrix equation involving the original parity values of the code and the polynomial of the digital filter's transfer function is formed, and row manipulations separate this equation into a set of homogeneous equations constraining the modifying scaling coefficients and another set which defines the code parity values' implementation.

  17. Stability Training for Convolutional Neural Nets in LArTPC

    NASA Astrophysics Data System (ADS)

    Lindsay, Matt; Wongjirad, Taritree

    2017-01-01

    Convolutional Neural Nets (CNNs) are the state of the art for many problems in computer vision and are a promising method for classifying interactions in Liquid Argon Time Projection Chambers (LArTPCs) used in neutrino oscillation experiments. Despite the good performance of CNN's, they are not without drawbacks, chief among them is vulnerability to noise and small perturbations to the input. One solution to this problem is a modification to the learning process called Stability Training developed by Zheng et al. We verify existing work and demonstrate volatility caused by simple Gaussian noise and also that the volatility can be nearly eliminated with Stability Training. We then go further and show that a traditional CNN is also vulnerable to realistic experimental noise and that a stability trained CNN remains accurate despite noise. This further adds to the optimism for CNNs for work in LArTPCs and other applications.

  18. Convolutional Neural Networks for patient-specific ECG classification.

    PubMed

    Kiranyaz, Serkan; Ince, Turker; Hamila, Ridha; Gabbouj, Moncef

    2015-01-01

    We propose a fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system using an adaptive implementation of 1D Convolutional Neural Networks (CNNs) that can fuse feature extraction and classification into a unified learner. In this way, a dedicated CNN will be trained for each patient by using relatively small common and patient-specific training data and thus it can also be used to classify long ECG records such as Holter registers in a fast and accurate manner. Alternatively, such a solution can conveniently be used for real-time ECG monitoring and early alert system on a light-weight wearable device. The experimental results demonstrate that the proposed system achieves a superior classification performance for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB).

  19. $\\mathtt {Deepr}$: A Convolutional Net for Medical Records.

    PubMed

    Nguyen, Phuoc; Tran, Truyen; Wickramasinghe, Nilmini; Venkatesh, Svetha

    2017-01-01

    Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space.

  20. Fast convolution with free-space Green's functions

    NASA Astrophysics Data System (ADS)

    Vico, Felipe; Greengard, Leslie; Ferrando, Miguel

    2016-10-01

    We introduce a fast algorithm for computing volume potentials - that is, the convolution of a translation invariant, free-space Green's function with a compactly supported source distribution defined on a uniform grid. The algorithm relies on regularizing the Fourier transform of the Green's function by cutting off the interaction in physical space beyond the domain of interest. This permits the straightforward application of trapezoidal quadrature and the standard FFT, with superalgebraic convergence for smooth data. Moreover, the method can be interpreted as employing a Nystrom discretization of the corresponding integral operator, with matrix entries which can be obtained explicitly and rapidly. This is of use in the design of preconditioners or fast direct solvers for a variety of volume integral equations. The method proposed permits the computation of any derivative of the potential, at the cost of an additional FFT.

  1. Rapid Exact Signal Scanning With Deep Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Thom, Markus; Gritschneder, Franz

    2017-03-01

    A rigorous formulation of the dynamics of a signal processing scheme aimed at dense signal scanning without any loss in accuracy is introduced and analyzed. Related methods proposed in the recent past lack a satisfactory analysis of whether they actually fulfill any exactness constraints. This is improved through an exact characterization of the requirements for a sound sliding window approach. The tools developed in this paper are especially beneficial if Convolutional Neural Networks are employed, but can also be used as a more general framework to validate related approaches to signal scanning. The proposed theory helps to eliminate redundant computations and renders special case treatment unnecessary, resulting in a dramatic boost in efficiency particularly on massively parallel processors. This is demonstrated both theoretically in a computational complexity analysis and empirically on modern parallel processors.

  2. Plane-wave decomposition by spherical-convolution microphone array

    NASA Astrophysics Data System (ADS)

    Rafaely, Boaz; Park, Munhum

    2004-05-01

    Reverberant sound fields are widely studied, as they have a significant influence on the acoustic performance of enclosures in a variety of applications. For example, the intelligibility of speech in lecture rooms, the quality of music in auditoria, the noise level in offices, and the production of 3D sound in living rooms are all affected by the enclosed sound field. These sound fields are typically studied through frequency response measurements or statistical measures such as reverberation time, which do not provide detailed spatial information. The aim of the work presented in this seminar is the detailed analysis of reverberant sound fields. A measurement and analysis system based on acoustic theory and signal processing, designed around a spherical microphone array, is presented. Detailed analysis is achieved by decomposition of the sound field into waves, using spherical Fourier transform and spherical convolution. The presentation will include theoretical review, simulation studies, and initial experimental results.

  3. Truncation Depth Rule-of-Thumb for Convolutional Codes

    NASA Technical Reports Server (NTRS)

    Moision, Bruce

    2009-01-01

    In this innovation, it is shown that a commonly used rule of thumb (that the truncation depth of a convolutional code should be five times the memory length, m, of the code) is accurate only for rate 1/2 codes. In fact, the truncation depth should be 2.5 m/(1 - r), where r is the code rate. The accuracy of this new rule is demonstrated by tabulating the distance properties of a large set of known codes. This new rule was derived by bounding the losses due to truncation as a function of the code rate. With regard to particular codes, a good indicator of the required truncation depth is the path length at which all paths that diverge from a particular path have accumulated the minimum distance of the code. It is shown that the new rule of thumb provides an accurate prediction of this depth for codes of varying rates.

  4. Radio frequency interference mitigation using deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Akeret, J.; Chang, C.; Lucchi, A.; Refregier, A.

    2017-01-01

    We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE &SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK's SUMTHRESHOLD implementation. We publish our U-Net software package on GitHub under GPLv3 license.

  5. Star-galaxy classification using deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Kim, Edward J.; Brunner, Robert J.

    2017-02-01

    Most existing star-galaxy classifiers use the reduced summary information from catalogues, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks (ConvNets) allow a machine to automatically learn the features directly from the data, minimizing the need for input from human experts. We present a star-galaxy classification framework that uses deep ConvNets directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey and the Canada-France-Hawaii Telescope Lensing Survey, we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey and the Large Synoptic Survey Telescope, because deep neural networks require very little, manual feature engineering.

  6. Novel WSi/Au T-shaped gate GaAs metal-semiconductor field-effect-transistor fabrication process for super low-noise microwave monolithic integrated circuit amplifiers

    SciTech Connect

    Takano, H.; Hosogi, K.; Kato, T.

    1995-05-01

    A fully ion-implanted self-aligned T-shaped gate Ga As metal-semiconductor field-effect transistor (MESFET) with high frequency and extremely low-noise performance has been successfully fabricated for super low-noise microwave monolithic integrated circuit (MMIC) amplifiers. A subhalf-micrometer gate structure composed of WSi/Ti/Mo/Au is employed to reduce gate resistance effectively. This multilayer gate structure is formed by newly developed dummy SiON self-alignment technology and a photoresist planarization process. At an operating frequency of 12 GHz, a minimum noise figure of 0.87 dB with an associated gain of 10.62 dB has been obtained. Based on the novel FET process, a low-noise single-stage MMIC amplifier with an excellent low-noise figure of 1.2 dB with an associated gain of 8 dB in the 14 GHz band has been realized. This is the lowest noise figure ever reported at this frequency for low-noise MMICs based on ion-implanted self-aligned gate MESFET technology. 14 refs., 9 figs.

  7. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.

    PubMed

    He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing; Sun, Jian

    2015-09-01

    Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.

  8. Bayesian Vision for Shape Recovery

    NASA Astrophysics Data System (ADS)

    Jalobeanu, André

    2004-11-01

    We present a new Bayesian vision technique that aims at recovering a shape from two or more noisy observations taken under similar lighting conditions. The shape is parametrized by a piecewise linear height field, textured by a piecewise linear irradiance field, and we assume Gaussian Markovian priors for both shape vertices and irradiance variables. The modeled observation process, equivalent to rendering, is modeled by a non-affine projection (e.g. perspective projection) followed by a convolution with a piecewise linear point spread function, and contamination by additive Gaussian noise. We assume that the observation parameters are calibrated beforehand. The major novelty of the proposed method consists of marginalizing out the irradiances considered as nuisance parameters, which is achieved by a hierarchy of approximations. This reduces the inference to minimizing an energy that only depends on the shape vertices, and therefore allows an efficient Iterated Conditional Mode (ICM) optimization scheme to be implemented. A Gaussian approximation of the posterior shape density is computed, thus providing estimates of both the geometry and its uncertainty. We illustrate the effectiveness of the new method by shape reconstruction results in a 2D case. A 3D version is currently under development and aims at recovering a surface from multiple images, reconstructing the topography by marginalizing out both albedo and shading.

  9. Bayesian Vision for Shape Recovery

    NASA Technical Reports Server (NTRS)

    Jalobeanu, Andre

    2004-01-01

    We present a new Bayesian vision technique that aims at recovering a shape from two or more noisy observations taken under similar lighting conditions. The shape is parametrized by a piecewise linear height field, textured by a piecewise linear irradiance field, and we assume Gaussian Markovian priors for both shape vertices and irradiance variables. The observation process. also known as rendering, is modeled by a non-affine projection (e.g. perspective projection) followed by a convolution with a piecewise linear point spread function. and contamination by additive Gaussian noise. We assume that the observation parameters are calibrated beforehand. The major novelty of the proposed method consists of marginalizing out the irradiances considered as nuisance parameters, which is achieved by Laplace approximations. This reduces the inference to minimizing an energy that only depends on the shape vertices, and therefore allows an efficient Iterated Conditional Mode (ICM) optimization scheme to be implemented. A Gaussian approximation of the posterior shape density is computed, thus providing estimates both the geometry and its uncertainty. We illustrate the effectiveness of the new method by shape reconstruction results in a 2D case. A 3D version is currently under development and aims at recovering a surface from multiple images, reconstructing the topography by marginalizing out both albedo and shading.

  10. Cygrid: A fast Cython-powered convolution-based gridding module for Python

    NASA Astrophysics Data System (ADS)

    Winkel, B.; Lenz, D.; Flöer, L.

    2016-06-01

    Context. Data gridding is a common task in astronomy and many other science disciplines. It refers to the resampling of irregularly sampled data to a regular grid. Aims: We present cygrid, a library module for the general purpose programming language Python. Cygrid can be used to resample data to any collection of target coordinates, although its typical application involves FITS maps or data cubes. The FITS world coordinate system standard is supported. Methods: The regridding algorithm is based on the convolution of the original samples with a kernel of arbitrary shape. We introduce a lookup table scheme that allows us to parallelize the gridding and combine it with the HEALPix tessellation of the sphere for fast neighbor searches. Results: We show that for n input data points, cygrids runtime scales between O(n) and O(nlog n) and analyze the performance gain that is achieved using multiple CPU cores. We also compare the gridding speed with other techniques, such as nearest-neighbor, and linear and cubic spline interpolation. Conclusions: Cygrid is a very fast and versatile gridding library that significantly outperforms other third-party Python modules, such as the linear and cubic spline interpolation provided by SciPy. http://https://github.com/bwinkel/cygrid

  11. Mechanics of a Knitted Fabric

    NASA Astrophysics Data System (ADS)

    Poincloux, Samuel; Lechenault, Frederic; Adda-Bedia, Mokhtar

    A simple knitted fabric can be seen as a topologically constrained slender rod following a periodic path. The non-linear properties of the fabric, such as large reversible deformation and characteristic shape under stress, arise from topological features known as stitches and are distinct from the constitutive yarn properties. Through experiments we studied a model stockinette fabric made of a single elastic thread, where the mechanical properties and local stitch displacements were measured. Then, we derived a model based on the yarn bending energy at the stitch level resulting in an evaluation of the displacement fields of the repetitive units which describe the fabric shape. The comparison between the predicted and the measured shape gives very good agreement and the right order of magnitude for the mechanical response is captured. This work aims at providing a fundamental framework for the understanding of knitted systems, paving the way to thread based smart materials. Contract ANR-14-CE07-0031-01 METAMAT.

  12. Some physical factors influencing the accuracy of convolution scatter correction in SPECT.

    PubMed

    Msaki, P; Axelsson, B; Larsson, S A

    1989-03-01

    Some important physical factors influencing the accuracy of convolution scatter correction techniques in SPECT are presented. In these techniques scatter correction in the projection relies on filter functions, QF, evaluated by Fourier transforms, from measured scatter functions, Qp, obtained from point spread functions. The spatial resolution has a marginal effect on Qp. Thus a single QF can be used in the scatter correction of SPECT measurements acquired with the low energy high resolution or the low energy general purpose collimators and over a wide range of patient-collimator distances. However, it is necessary to examine the details of the shape of point spread functions during evaluation of Qp. QF is completely described by scatter amplitude AF, slope BF and filter sum SF. SF is obtained by summation of the values of QF occupying a 31 x 31 pixels matrix. Regardless of differences in amplitude and slope, two filter functions are shown to be equivalent in terms of scatter correction ability, whenever their sums are equal. On the basis of filter sum, the observed small influence of ellipticity on QF implies that an average function can be used in scatter correcting SPECT measurements conducted with elliptic objects. SF is shown to increase with a decrease in photon energy and with an increase in window size. Thus, scatter correction by convolution may be severely hampered by photon statistics when SPECT imaging is done with low-energy photons. It is pointless to use unnecessarily large discriminator windows, in the hope of improving photon statistics, since most of the extra events acquired will eventually be subtracted during scatter correction. Regardless of the observed moderate reduction in SF when a lung-equivalent material replaces a portion of a water phantom, further studies are needed to develop a technique that is capable of handling attenuation and scatter corrections simultaneously. Whenever superficial and inner radioactive distributions coexist the

  13. In Situ Fabrication Technologies

    NASA Technical Reports Server (NTRS)

    Rolin, Terry D.; Hammond, Monica

    2005-01-01

    A manufacturing system is described that is internal to controlled cabin environments which will produce functional parts to net shape with sufficient tolerance, strength and integrity to meet application specific needs such as CEV ECLS components, robotic arm or rover components, EVA suit items, unforeseen tools, conformal repair patches, and habitat fittings among others. Except for start-up and shut-down, fabrication will be automatic without crew intervention under nominal scenarios. Off-nominal scenarios may require crew and/or Earth control intervention. System will have the ability to fabricate using both provisioned feedstock materials and feedstock refined from in situ regolith.

  14. Optimal convolution SOR acceleration of waveform relaxation with application to semiconductor device simulation

    NASA Technical Reports Server (NTRS)

    Reichelt, Mark

    1993-01-01

    In this paper we describe a novel generalized SOR (successive overrelaxation) algorithm for accelerating the convergence of the dynamic iteration method known as waveform relaxation. A new convolution SOR algorithm is presented, along with a theorem for determining the optimal convolution SOR parameter. Both analytic and experimental results are given to demonstrate that the convergence of the convolution SOR algorithm is substantially faster than that of the more obvious frequency-independent waveform SOR algorithm. Finally, to demonstrate the general applicability of this new method, it is used to solve the differential-algebraic system generated by spatial discretization of the time-dependent semiconductor device equations.

  15. Intraocular lens fabrication

    DOEpatents

    Salazar, Mike A.; Foreman, Larry R.

    1997-01-01

    This invention describes a method for fabricating an intraocular lens made rom clear Teflon.TM., Mylar.TM., or other thermoplastic material having a thickness of about 0.025 millimeters. These plastic materials are thermoformable and biocompatable with the human eye. The two shaped lenses are bonded together with a variety of procedures which may include thermosetting and solvent based adhesives, laser and impulse welding, and ultrasonic bonding. The fill tube, which is used to inject a refractive filling material is formed with the lens so as not to damage the lens shape. A hypodermic tube may be included inside the fill tube.

  16. Intraocular lens fabrication

    DOEpatents

    Salazar, M.A.; Foreman, L.R.

    1997-07-08

    This invention describes a method for fabricating an intraocular lens made from clear Teflon{trademark}, Mylar{trademark}, or other thermoplastic material having a thickness of about 0.025 millimeters. These plastic materials are thermoformable and biocompatable with the human eye. The two shaped lenses are bonded together with a variety of procedures which may include thermosetting and solvent based adhesives, laser and impulse welding, and ultrasonic bonding. The fill tube, which is used to inject a refractive filling material is formed with the lens so as not to damage the lens shape. A hypodermic tube may be included inside the fill tube. 13 figs.

  17. Operational and convolution properties of two-dimensional Fourier transforms in polar coordinates.

    PubMed

    Baddour, Natalie

    2009-08-01

    For functions that are best described in terms of polar coordinates, the two-dimensional Fourier transform can be written in terms of polar coordinates as a combination of Hankel transforms and Fourier series-even if the function does not possess circular symmetry. However, to be as useful as its Cartesian counterpart, a polar version of the Fourier operational toolset is required for the standard operations of shift, multiplication, convolution, etc. This paper derives the requisite polar version of the standard Fourier operations. In particular, convolution-two dimensional, circular, and radial one dimensional-is discussed in detail. It is shown that standard multiplication/convolution rules do apply as long as the correct definition of convolution is applied.

  18. Convoluted nozzle design for the RL10 derivative 2B engine

    NASA Technical Reports Server (NTRS)

    1985-01-01

    The convoluted nozzle is a conventional refractory metal nozzle extension that is formed with a portion of the nozzle convoluted to show the extendible nozzle within the length of the rocket engine. The convoluted nozzle (CN) was deployed by a system of four gas driven actuators. For spacecraft applications the optimum CN may be self-deployed by internal pressure retained, during deployment, by a jettisonable exit closure. The convoluted nozzle is included in a study of extendible nozzles for the RL10 Engine Derivative 2B for use in an early orbit transfer vehicle (OTV). Four extendible nozzle configurations for the RL10-2B engine were evaluated. Three configurations of the two position nozzle were studied including a hydrogen dump cooled metal nozzle and radiation cooled nozzles of refractory metal and carbon/carbon composite construction respectively.

  19. Convolution of large 3D images on GPU and its decomposition

    NASA Astrophysics Data System (ADS)

    Karas, Pavel; Svoboda, David

    2011-12-01

    In this article, we propose a method for computing convolution of large 3D images. The convolution is performed in a frequency domain using a convolution theorem. The algorithm is accelerated on a graphic card by means of the CUDA parallel computing model. Convolution is decomposed in a frequency domain using the decimation in frequency algorithm. We pay attention to keeping our approach efficient in terms of both time and memory consumption and also in terms of memory transfers between CPU and GPU which have a significant inuence on overall computational time. We also study the implementation on multiple GPUs and compare the results between the multi-GPU and multi-CPU implementations.

  20. Directional Radiometry and Radiative Transfer: the Convoluted Path From Centuries-old Phenomenology to Physical Optics

    NASA Technical Reports Server (NTRS)

    Mishchenko, Michael I.

    2014-01-01

    This Essay traces the centuries-long history of the phenomenological disciplines of directional radiometry and radiative transfer in turbid media, discusses their fundamental weaknesses, and outlines the convoluted process of their conversion into legitimate branches of physical optics.

  1. Forecasting natural aquifer discharge using a numerical model and convolution.

    PubMed

    Boggs, Kevin G; Johnson, Gary S; Van Kirk, Rob; Fairley, Jerry P

    2014-01-01

    If the nature of groundwater sources and sinks can be determined or predicted, the data can be used to forecast natural aquifer discharge. We present a procedure to forecast the relative contribution of individual aquifer sources and sinks to natural aquifer discharge. Using these individual aquifer recharge components, along with observed aquifer heads for each January, we generate a 1-year, monthly spring discharge forecast for the upcoming year with an existing numerical model and convolution. The results indicate that a forecast of natural aquifer discharge can be developed using only the dominant aquifer recharge sources combined with the effects of aquifer heads (initial conditions) at the time the forecast is generated. We also estimate how our forecast will perform in the future using a jackknife procedure, which indicates that the future performance of the forecast is good (Nash-Sutcliffe efficiency of 0.81). We develop a forecast and demonstrate important features of the procedure by presenting an application to the Eastern Snake Plain Aquifer in southern Idaho.

  2. Remote Sensing Image Fusion with Convolutional Neural Network

    NASA Astrophysics Data System (ADS)

    Zhong, Jinying; Yang, Bin; Huang, Guoyu; Zhong, Fei; Chen, Zhongze

    2016-12-01

    Remote sensing image fusion (RSIF) is referenced as restoring the high-resolution multispectral image from its corresponding low-resolution multispectral (LMS) image aided by the panchromatic (PAN) image. Most RSIF methods assume that the missing spatial details of the LMS image can be obtained from the high resolution PAN image. However, the distortions would be produced due to the much difference between the structural component of LMS image and that of PAN image. Actually, the LMS image can fully utilize its spatial details to improve the resolution. In this paper, a novel two-stage RSIF algorithm is proposed, which makes full use of both spatial details and spectral information of the LMS image itself. In the first stage, the convolutional neural network based super-resolution is used to increase the spatial resolution of the LMS image. In the second stage, Gram-Schmidt transform is employed to fuse the enhanced MS and the PAN images for further improvement the resolution of MS image. Since the spatial resolution enhancement in the first stage, the spectral distortions in the fused image would be decreased in evidence. Moreover, the spatial details can be preserved to construct the fused images. The QuickBird satellite source images are used to test the performances of the proposed method. The experimental results demonstrate that the proposed method can achieve better spatial details and spectral information simultaneously compared with other well-known methods.

  3. Delta function convolution method (DFCM) for fluorescence decay experiments

    NASA Astrophysics Data System (ADS)

    Zuker, M.; Szabo, A. G.; Bramall, L.; Krajcarski, D. T.; Selinger, B.

    1985-01-01

    A rigorous and convenient method of correcting for the wavelength variation of the instrument response function in time correlated photon counting fluorescence decay measurements is described. The method involves convolution of a modified functional form F˜s of the physical model with a reference data set measured under identical conditions as the measurement of the sample. The method is completely general in that an appropriate functional form may be found for any physical model of the excited state decay process. The modified function includes a term which is a Dirac delta function and terms which give the correct decay times and preexponential values in which one is interested. None of the data is altered in any way, permitting correct statistical analysis of the fitting. The method is readily adaptable to standard deconvolution procedures. The paper describes the theory and application of the method together with fluorescence decay results obtained from measurements of a number of different samples including diphenylhexatriene, myoglobin, hemoglobin, 4', 6-diamidine-2-phenylindole (DAPI), and lysine-trytophan-lysine.

  4. Visualizing Flow Over Parametric Surfaces Using Line Integral Convolution

    NASA Technical Reports Server (NTRS)

    Forssell, Lisa; Lasinski, T. A. (Technical Monitor)

    1994-01-01

    Line Integral Convolution (LIC) is a powerful technique for imaging and animating vector fields. We extend the LIC paradigm in three ways: (1) The existing technique is limited to vector fields over a regular Cartesian grid. We extend it to vector fields over parametric surfaces, such as those found in curvilinear grids, used in computational fluid dynamics simulations; (2) Periodic motion filters can be used to animate the flow visualization. When the flow lies on a parametric surface, however, the motion appears misleading. We explain why this problem arises and show how to adjust the LIC algorithm to handle it; (3) We introduce a technique to visualize vector magnitudes as well as vector direction. Cabral and Leedom have suggested a method for variable-speed animation, which is based on varying the frequency of the filter function. We develop a different technique based on kernel phase shifts which we have found to show substantially better results. Our implementation of these algorithms utilizes texture-mapping hardware to run in real time, which allows them to be included in interactive applications.

  5. Interleaved convolutional coding for the turbulent atmospheric optical communication channel

    NASA Astrophysics Data System (ADS)

    Davidson, Frederic M.; Koh, Yutai T.

    1988-09-01

    The coding gain of a constraint-length-three, rate one-half convolutional code over a long clear-air atmospheric direct-detection optical communication channel using binary pulse-position modulation signaling was directly measured as a function of interleaving delay for both hard- and soft-decision Viterbi decoding. Maximum coding gains theoretically possible for this code with perfect interleaving and physically unrealizable perfect-measurement decoding were about 7 dB under conditions of weak clear-air turbulence, and 11 dB at moderate turbulence levels. The time scale of the fading (memory) of the channel was directly measured to be tens to hundreds of milliseconds, depending on turbulence levels. Interleaving delays of 5 ms between transmission of the first and second channel bits output by the encoder yield coding gains within 1.5 dB of theoretical limits with soft-decision Viterbi decoding. Coding gains of 4-5 dB were observed with only 100 microseconds of interleaving delay. Soft-decision Viterbi decoding always yielded 1-2 dB more coding gain than hard-decision Viterbi decoding.

  6. Multi-resolution Convolution Methodology for ICP Waveform Morphology Analysis.

    PubMed

    Shaw, Martin; Piper, Ian; Hawthorne, Christopher

    2016-01-01

    Intracranial pressure (ICP) monitoring is a key clinical tool in the assessment and treatment of patients in neurointensive care. ICP morphology analysis can be useful in the classification of waveform features.A methodology for the decomposition of an ICP signal into clinically relevant dimensions has been devised that allows the identification of important ICP waveform types. It has three main components. First, multi-resolution convolution analysis is used for the main signal decomposition. Then, an impulse function is created, with multiple parameters, that can represent any form in the signal under analysis. Finally, a simple, localised optimisation technique is used to find morphologies of interest in the decomposed data.A pilot application of this methodology using a simple signal has been performed. This has shown that the technique works with performance receiver operator characteristic area under the curve values for each of the waveform types: plateau wave, B wave and high and low compliance states of 0.936, 0.694, 0.676 and 0.698, respectively.This is a novel technique that showed some promise during the pilot analysis. However, it requires further optimisation to become a usable clinical tool for the automated analysis of ICP signals.

  7. Toward an optimal convolutional neural network for traffic sign recognition

    NASA Astrophysics Data System (ADS)

    Habibi Aghdam, Hamed; Jahani Heravi, Elnaz; Puig, Domenec

    2015-12-01

    Convolutional Neural Networks (CNN) beat the human performance on German Traffic Sign Benchmark competition. Both the winner and the runner-up teams trained CNNs to recognize 43 traffic signs. However, both networks are not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecture that reduces the number of the parameters 27% and 22% compared with the two networks. Furthermore, our network uses Leaky Rectified Linear Units (ReLU) as the activation function that only needs a few operations to produce the result. Specifically, compared with the hyperbolic tangent and rectified sigmoid activation functions utilized in the two networks, Leaky ReLU needs only one multiplication operation which makes it computationally much more efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best reported classification accuracy while it reduces the overall number of parameters 85% compared with the winner network in the competition.

  8. Study of multispectral convolution scatter correction in high resolution PET

    SciTech Connect

    Yao, R.; Lecomte, R.; Bentourkia, M.

    1996-12-31

    PET images acquired with a high resolution scanner based on arrays of small discrete detectors are obtained at the cost of low sensitivity and increased detector scatter. It has been postulated that these limitations can be overcome by using enlarged discrimination windows to include more low energy events and by developing more efficient energy-dependent methods to correct for scatter. In this work, we investigate one such method based on the frame-by-frame scatter correction of multispectral data. Images acquired in the conventional, broad and multispectral window modes were processed by the stationary and nonstationary consecutive convolution scatter correction methods. Broad and multispectral window acquisition with a low energy threshold of 129 keV improved system sensitivity by up to 75% relative to conventional window with a {approximately}350 keV threshold. The degradation of image quality due to the added scatter events can almost be fully recovered by the subtraction-restoration scatter correction. The multispectral method was found to be more sensitive to the nonstationarity of scatter and its performance was not as good as that of the broad window. It is concluded that new scatter degradation models and correction methods need to be established to fully take advantage of multispectral data.

  9. Cell volume regulation in the proximal convoluted tubule.

    PubMed

    Gagnon, J; Ouimet, D; Nguyen, H; Laprade, R; Le Grimellec, C; Carrière, S; Cardinal, J

    1982-10-01

    To evaluate the effect of hyper- and hypotonicity on proximal convoluted tubule (PCT) cell volume, nonperfused PCT were studied in vitro with hypertonic solutions containing sodium chloride, urea, or mannitol (450 mosmol/kg H2O) and with hypotonic low sodium chloride solutions (160 mosmol/kg H2O). When the tubules were subjected to hypertonic peritubular solutions containing NaCl, cell volume immediately decreased by 15.5% and remained constant throughout the experimental period (60 min). With mannitol, the initial decrease was identical to that with NaCl (17.7%), but the PCT volume increased slightly during the experimental period. With urea, the decrease in cell volume was smaller (7%) and transient. In hypotonicity, the PCT swelled rapidly, but this swelling was followed by a rapid regulatory phase in which PCT volume nearly returned to control values after less than 10 min. With a potassium-free peritubular medium or 10(-3) M ouabain, the regulatory phase of hypotonicity completely disappeared, whereas the cells did not maintain their reduced volume in NaCl-induced hypertonicity. These results suggest that Na-K-ATPase plays an important role in the maintenance of a reduced cellular volume in hypertonicity and in the regulatory phase of hypotonicity, probably by an active extrusion of sodium and water from the cell.

  10. Convolutional networks for fast, energy-efficient neuromorphic computing

    PubMed Central

    Esser, Steven K.; Merolla, Paul A.; Arthur, John V.; Cassidy, Andrew S.; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J.; McKinstry, Jeffrey L.; Melano, Timothy; Barch, Davis R.; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D.; Modha, Dharmendra S.

    2016-01-01

    Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer. PMID:27651489

  11. Method for Veterbi decoding of large constraint length convolutional codes

    NASA Technical Reports Server (NTRS)

    Hsu, In-Shek (Inventor); Truong, Trieu-Kie (Inventor); Reed, Irving S. (Inventor); Jing, Sun (Inventor)

    1988-01-01

    A new method of Viterbi decoding of convolutional codes lends itself to a pipline VLSI architecture using a single sequential processor to compute the path metrics in the Viterbi trellis. An array method is used to store the path information for NK intervals where N is a number, and K is constraint length. The selected path at the end of each NK interval is then selected from the last entry in the array. A trace-back method is used for returning to the beginning of the selected path back, i.e., to the first time unit of the interval NK to read out the stored branch metrics of the selected path which correspond to the message bits. The decoding decision made in this way is no longer maximum likelihood, but can be almost as good, provided that constraint length K in not too small. The advantage is that for a long message, it is not necessary to provide a large memory to store the trellis derived information until the end of the message to select the path that is to be decoded; the selection is made at the end of every NK time unit, thus decoding a long message in successive blocks.

  12. Deep convolutional neural networks for classifying GPR B-scans

    NASA Astrophysics Data System (ADS)

    Besaw, Lance E.; Stimac, Philip J.

    2015-05-01

    Symmetric and asymmetric buried explosive hazards (BEHs) present real, persistent, deadly threats on the modern battlefield. Current approaches to mitigate these threats rely on highly trained operatives to reliably detect BEHs with reasonable false alarm rates using handheld Ground Penetrating Radar (GPR) and metal detectors. As computers become smaller, faster and more efficient, there exists greater potential for automated threat detection based on state-of-the-art machine learning approaches, reducing the burden on the field operatives. Recent advancements in machine learning, specifically deep learning artificial neural networks, have led to significantly improved performance in pattern recognition tasks, such as object classification in digital images. Deep convolutional neural networks (CNNs) are used in this work to extract meaningful signatures from 2-dimensional (2-D) GPR B-scans and classify threats. The CNNs skip the traditional "feature engineering" step often associated with machine learning, and instead learn the feature representations directly from the 2-D data. A multi-antennae, handheld GPR with centimeter-accurate positioning data was used to collect shallow subsurface data over prepared lanes containing a wide range of BEHs. Several heuristics were used to prevent over-training, including cross validation, network weight regularization, and "dropout." Our results show that CNNs can extract meaningful features and accurately classify complex signatures contained in GPR B-scans, complementing existing GPR feature extraction and classification techniques.

  13. HEp-2 Cell Image Classification with Deep Convolutional Neural Networks.

    PubMed

    Gao, Zhimin; Wang, Lei; Zhou, Luping; Zhang, Jianjia

    2016-02-08

    Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. In addition to describing the proposed classification framework, this paper elaborates several interesting observations and findings obtained by our investigation. They include the important factors that impact network design and training, the role of rotation-based data augmentation for cell images, the effectiveness of cell image masks for classification, and the adaptability of the CNN-based classification system across different datasets. Extensive experimental study is conducted to verify the above findings and compares the proposed framework with the well-established image classification models in the literature. The results on benchmark datasets demonstrate that i) the proposed framework can effectively outperform existing models by properly applying data augmentation; ii) our CNN-based framework has excellent adaptability across different datasets, which is highly desirable for cell image classification under varying laboratory settings. Our system is ranked high in the cell image classification competition hosted by ICPR 2014.

  14. A quantum algorithm for Viterbi decoding of classical convolutional codes

    NASA Astrophysics Data System (ADS)

    Grice, Jon R.; Meyer, David A.

    2015-07-01

    We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper, the proposed algorithm is applied to decoding classical convolutional codes, for instance, large constraint length and short decode frames . Other applications of the classical Viterbi algorithm where is large (e.g., speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butterfly diagram of the fast Fourier transform, with its corresponding fast quantum algorithm. The tensor-product structure of the butterfly diagram corresponds to a quantum superposition that we show can be efficiently prepared. The quantum speedup is possible because the performance of the QVA depends on the fanout (number of possible transitions from any given state in the hidden Markov model) which is in general much less than . The QVA constructs a superposition of states which correspond to all legal paths through the decoding lattice, with phase as a function of the probability of the path being taken given received data. A specialized amplitude amplification procedure is applied one or more times to recover a superposition where the most probable path has a high probability of being measured.

  15. Designing the optimal convolution kernel for modeling the motion blur

    NASA Astrophysics Data System (ADS)

    Jelinek, Jan

    2011-06-01

    Motion blur acts on an image like a two dimensional low pass filter, whose spatial frequency characteristic depends both on the trajectory of the relative motion between the scene and the camera and on the velocity vector variation along it. When motion during exposure is permitted, the conventional, static notions of both the image exposure and the scene-toimage mapping become unsuitable and must be revised to accommodate the image formation dynamics. This paper develops an exact image formation model for arbitrary object-camera relative motion with arbitrary velocity profiles. Moreover, for any motion the camera may operate in either continuous or flutter shutter exposure mode. Its result is a convolution kernel, which is optimally designed for both the given motion and sensor array geometry, and hence permits the most accurate computational undoing of the blurring effects for the given camera required in forensic and high security applications. The theory has been implemented and a few examples are shown in the paper.

  16. A deep convolutional neural network for recognizing foods

    NASA Astrophysics Data System (ADS)

    Jahani Heravi, Elnaz; Habibi Aghdam, Hamed; Puig, Domenec

    2015-12-01

    Controlling the food intake is an efficient way that each person can undertake to tackle the obesity problem in countries worldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute their calories. State-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advances in large-scale object recognition datasets such as ImageNet have revealed that deep Convolutional Neural Networks (CNN) possess more representation power than the hand-crafted features. The main challenge with CNNs is to find the appropriate architecture for each problem. In this paper, we propose a deep CNN which consists of 769; 988 parameters. Our experiments show that the proposed CNN outperforms the state-of-art methods and improves the best result of traditional methods 17%. Moreover, using an ensemble of two CNNs that have been trained two different times, we are able to improve the classification performance 21:5%.

  17. Classification of breast cancer cytological specimen using convolutional neural network

    NASA Astrophysics Data System (ADS)

    Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

    2017-01-01

    The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

  18. Convolutional networks for fast, energy-efficient neuromorphic computing.

    PubMed

    Esser, Steven K; Merolla, Paul A; Arthur, John V; Cassidy, Andrew S; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J; McKinstry, Jeffrey L; Melano, Timothy; Barch, Davis R; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D; Modha, Dharmendra S

    2016-10-11

    Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

  19. A convolution model for computing the far-field directivity of a parametric loudspeaker array.

    PubMed

    Shi, Chuang; Kajikawa, Yoshinobu

    2015-02-01

    This paper describes a method to compute the far-field directivity of a parametric loudspeaker array (PLA), whereby the steerable parametric loudspeaker can be implemented when phased array techniques are applied. The convolution of the product directivity and the Westervelt's directivity is suggested, substituting for the past practice of using the product directivity only. Computed directivity of a PLA using the proposed convolution model achieves significant improvement in agreement to measured directivity at a negligible computational cost.

  20. Error Analysis of Padding Schemes for DFT’s of Convolutions and Derivatives

    DTIC Science & Technology

    2012-01-31

    Geodaetica, 18,263-279. Oppenheim AV, Schafer RW (1975) Digital Signal Processing . Prentice-Hall, Inc., Englewood Cliffs, New Jersey. Schwarz KP... Oppenheim and Schäfer (1975). Many numerical tests have been done to show that this so-called zero padding improves the computation of Stokes...and (19) relate linear convolutions to corresponding cyclic convolutions. Equation (19) is the justification, originating in Oppenheim and Schäfer

  1. Fabric fastenings

    NASA Technical Reports Server (NTRS)

    Walen, E D; Fisher, R T

    1920-01-01

    The study of aeronautical fabrics has led to a consideration of the best methods of attaching and fastening together such materials. This report presents the results of an investigation upon the proper methods of attaching fabrics to airplane wings. The methods recommended in this report have been adopted by the military services.

  2. Minimal-memory realization of pearl-necklace encoders of general quantum convolutional codes

    SciTech Connect

    Houshmand, Monireh; Hosseini-Khayat, Saied

    2011-02-15

    Quantum convolutional codes, like their classical counterparts, promise to offer higher error correction performance than block codes of equivalent encoding complexity, and are expected to find important applications in reliable quantum communication where a continuous stream of qubits is transmitted. Grassl and Roetteler devised an algorithm to encode a quantum convolutional code with a ''pearl-necklace'' encoder. Despite their algorithm's theoretical significance as a neat way of representing quantum convolutional codes, it is not well suited to practical realization. In fact, there is no straightforward way to implement any given pearl-necklace structure. This paper closes the gap between theoretical representation and practical implementation. In our previous work, we presented an efficient algorithm to find a minimal-memory realization of a pearl-necklace encoder for Calderbank-Shor-Steane (CSS) convolutional codes. This work is an extension of our previous work and presents an algorithm for turning a pearl-necklace encoder for a general (non-CSS) quantum convolutional code into a realizable quantum convolutional encoder. We show that a minimal-memory realization depends on the commutativity relations between the gate strings in the pearl-necklace encoder. We find a realization by means of a weighted graph which details the noncommutative paths through the pearl necklace. The weight of the longest path in this graph is equal to the minimal amount of memory needed to implement the encoder. The algorithm has a polynomial-time complexity in the number of gate strings in the pearl-necklace encoder.

  3. Modeling of woven fabric structures based on fourier image analysis.

    PubMed

    Escofet, J; Millán, M S; Ralló, M

    2001-12-01

    The periodic woven structures of fabrics can be defined on the basis of the convolution theorem. Here an elementary unit with the minimum number of thread crossings and a nonrectangular two-dimensional comb function for the pattern of repetition is used to define woven structures. The expression derived is more compact than the conventional diagram for weaving, and the parameters that one needs to determine a given fabric can easily be extracted from its Fourier transform. Several results with real samples of the most common structures-plain, twill, and satin-are presented.

  4. Text-Attentional Convolutional Neural Network for Scene Text Detection.

    PubMed

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-06-01

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/non-text information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates the main task of text/non-text classification. In addition, a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed, which extends the widely used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 data set, with an F-measure of 0.82, substantially improving the state-of-the-art results.

  5. Text-Attentional Convolutional Neural Networks for Scene Text Detection.

    PubMed

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-03-28

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.

  6. Output-sensitive 3D line integral convolution.

    PubMed

    Falk, Martin; Weiskopf, Daniel

    2008-01-01

    We propose an output-sensitive visualization method for 3D line integral convolution (LIC) whose rendering speed is largely independent of the data set size and mostly governed by the complexity of the output on the image plane. Our approach of view-dependent visualization tightly links the LIC generation with the volume rendering of the LIC result in order to avoid the computation of unnecessary LIC points: early-ray termination and empty-space leaping techniques are used to skip the computation of the LIC integral in a lazy-evaluation approach; both ray casting and texture slicing can be used as volume-rendering techniques. The input noise is modeled in object space to allow for temporal coherence under object and camera motion. Different noise models are discussed, covering dense representations based on filtered white noise all the way to sparse representations similar to oriented LIC. Aliasing artifacts are avoided by frequency control over the 3D noise and by employing a 3D variant of MIPmapping. A range of illumination models is applied to the LIC streamlines: different codimension-2 lighting models and a novel gradient-based illumination model that relies on precomputed gradients and does not require any direct calculation of gradients after the LIC integral is evaluated. We discuss the issue of proper sampling of the LIC and volume-rendering integrals by employing a frequency-space analysis of the noise model and the precomputed gradients. Finally, we demonstrate that our visualization approach lends itself to a fast graphics processing unit (GPU) implementation that supports both steady and unsteady flow. Therefore, this 3D LIC method allows users to interactively explore 3D flow by means of high-quality, view-dependent, and adaptive LIC volume visualization. Applications to flow visualization in combination with feature extraction and focus-and-context visualization are described, a comparison to previous methods is provided, and a detailed performance

  7. Deep convolutional networks for pancreas segmentation in CT imaging

    NASA Astrophysics Data System (ADS)

    Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

    2015-03-01

    Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

  8. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

    PubMed

    Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

    2016-05-01

    Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

  9. Brain Tumor Segmentation using Convolutional Neural Networks in MRI Images.

    PubMed

    Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

    2016-03-04

    Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 33 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0:88, 0:83, 0:77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0:78, 0:65, and 0:75 for the complete, core, and enhancing regions, respectively.

  10. A staggered-grid convolutional differentiator for elastic wave modelling

    NASA Astrophysics Data System (ADS)

    Sun, Weijia; Zhou, Binzhong; Fu, Li-Yun

    2015-11-01

    The computation of derivatives in governing partial differential equations is one of the most investigated subjects in the numerical simulation of physical wave propagation. An analytical staggered-grid convolutional differentiator (CD) for first-order velocity-stress elastic wave equations is derived in this paper by inverse Fourier transformation of the band-limited spectrum of a first derivative operator. A taper window function is used to truncate the infinite staggered-grid CD stencil. The truncated CD operator is almost as accurate as the analytical solution, and as efficient as the finite-difference (FD) method. The selection of window functions will influence the accuracy of the CD operator in wave simulation. We search for the optimal Gaussian windows for different order CDs by minimizing the spectral error of the derivative and comparing the windows with the normal Hanning window function for tapering the CD operators. It is found that the optimal Gaussian window appears to be similar to the Hanning window function for tapering the same CD operator. We investigate the accuracy of the windowed CD operator and the staggered-grid FD method with different orders. Compared to the conventional staggered-grid FD method, a short staggered-grid CD operator achieves an accuracy equivalent to that of a long FD operator, with lower computational costs. For example, an 8th order staggered-grid CD operator can achieve the same accuracy of a 16th order staggered-grid FD algorithm but with half of the computational resources and time required. Numerical examples from a homogeneous model and a crustal waveguide model are used to illustrate the superiority of the CD operators over the conventional staggered-grid FD operators for the simulation of wave propagations.

  11. Single-trial EEG RSVP classification using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

    2016-05-01

    Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

  12. Self-shaping of bioinspired chiral composites

    NASA Astrophysics Data System (ADS)

    Rong, Qing-Qing; Cui, Yu-Hong; Shimada, Takahiro; Wang, Jian-Shan; Kitamura, Takayuki

    2014-08-01

    Self-shaping materials such as shape memory polymers have recently drawn considerable attention owing to their high shape-changing ability in response to changes in ambient conditions, and thereby have promising applications in the biomedical, biosensing, soft robotics and aerospace fields. Their design is a crucial issue of both theoretical and technological interest. Motivated by the shape-changing ability of Towel Gourd tendril helices during swelling/deswelling, we present a strategy for realizing self-shaping function through the deformation of micro/nanohelices. To guide the design and fabrication of self-shaping materials, the shape equations of bent configurations, twisted belts, and helices of slender chiral composite are developed using the variation method. Furthermore, it is numerically shown that the shape changes of a chiral composite can be tuned by the deformation of micro/nanohelices and the fabricated fiber directions. This work paves a new way to create self-shaping composites.

  13. Fabrication of Molded Magnetic Article

    NASA Technical Reports Server (NTRS)

    Bryant, Robert G. (Inventor); Namkung, Min (Inventor); Wincheski, Russell A. (Inventor); Fox, Robert L. (Inventor)

    2001-01-01

    A molded magnetic article and fabrication method are provided. Particles of ferromagnetic material embedded in a polymer binder are molded under heat and pressure into a geometric shape. Each particle is an oblate spheroid having a radius-to-thickness aspect ratio approximately in the range of 15-30. Each oblate spheroid has flattened poles that are substantially in perpendicular alignment to a direction of the molding pressure throughout the geometric shape.

  14. Convolution effect on TCR log response curve and the correction method for it

    NASA Astrophysics Data System (ADS)

    Chen, Q.; Liu, L. J.; Gao, J.

    2016-09-01

    Through-casing resistivity (TCR) logging has been successfully used in production wells for the dynamic monitoring of oil pools and the distribution of the residual oil, but its vertical resolution has limited its efficiency in identification of thin beds. The vertical resolution is limited by the distortion phenomenon of vertical response of TCR logging. The distortion phenomenon was studied in this work. It was found that the vertical response curve of TCR logging is the convolution of the true formation resistivity and the convolution function of TCR logging tool. Due to the effect of convolution, the measurement error at thin beds can reach 30% or even bigger. Thus the information of thin bed might be covered up very likely. The convolution function of TCR logging tool was obtained in both continuous and discrete way in this work. Through modified Lyle-Kalman deconvolution method, the true formation resistivity can be optimally estimated, so this inverse algorithm can correct the error caused by the convolution effect. Thus it can improve the vertical resolution of TCR logging tool for identification of thin beds.

  15. Directed light fabrication

    SciTech Connect

    Lewis, G.K.; Nemec, R.; Milewski, J.; Thoma, D.J.; Cremers, D.; Barbe, M.

    1994-09-01

    Directed Light Fabrication (DLF) is a rapid prototyping process being developed at Los Alamos National Laboratory to fabricate metal components. This is done by fusing gas delivered metal powder particles in the focal zone of a laser beam that is, programmed to move along or across the part cross section. Fully dense metal is built up a layer at a time to form the desired part represented by a 3 dimensional solid model from CAD software. Machine ``tool paths`` are created from the solid model that command the movement and processing parameters specific to the DLF process so that the part can be built one layer at a time. The result is a fully dense, near net shape metal part that solidifies under rapid solidification conditions.

  16. Directed light fabrication

    NASA Astrophysics Data System (ADS)

    Lewis, G. K.; Nemec, R.; Milewski, J.; Thoma, D. J.; Cremers, D.; Barbe, M.

    1994-09-01

    Directed Light Fabrication (DLF) is a rapid prototyping process being developed at Los Alamos National Laboratory to fabricate metal components. This is done by fusing gas delivered metal powder particles in the focal zone of a laser beam that is programmed to move along or across the part cross section. Fully dense metal is built up a layer at a time to form the desired part represented by a 3 dimensional solid model from CAD software. Machine 'tool paths' are created from the solid model that command the movement and processing parameters specific to the DLF process so that the part can be built one layer at a time. The result is a fully dense, near net shape metal part that solidifies under rapid solidification conditions.

  17. [Application of numerical convolution in in vivo/in vitro correlation research].

    PubMed

    Yue, Peng

    2009-01-01

    This paper introduced the conception and principle of in vivo/in vitro correlation (IVIVC) and convolution/deconvolution methods, and elucidated in details the convolution strategy and method for calculating the in vivo absorption performance of the pharmaceutics according to the their pharmacokinetic data in Excel, then put the results forward to IVIVC research. Firstly, the pharmacokinetic data ware fitted by mathematical software to make up the lost points. Secondly, the parameters of the optimal fitted input function were defined by trail-and-error method according to the convolution principle in Excel under the hypothesis that all the input functions fit the Weibull functions. Finally, the IVIVC between in vivo input function and the in vitro dissolution was studied. In the examples, not only the application of this method was demonstrated in details but also its simplicity and effectiveness were proved by comparing with the compartment model method and deconvolution method. It showed to be a powerful tool for IVIVC research.

  18. Fabrication of metallic glass structures

    DOEpatents

    Cline, C.F.

    1983-10-20

    Amorphous metal powders or ribbons are fabricated into solid shapes of appreciable thickness by the application of compaction energy. The temperature regime wherein the amorphous metal deforms by viscous flow is measured. The metal powders or ribbons are compacted within the temperature regime.

  19. Fabrication of metallic glass structures

    DOEpatents

    Cline, Carl F.

    1986-01-01

    Amorphous metal powders or ribbons are fabricated into solid shapes of appreciable thickness by the application of compaction energy. The temperature regime wherein the amorphous metal deforms by viscous flow is measured. The metal powders or ribbons are compacted within the temperature range.

  20. The Brain's Representations May Be Compatible With Convolution-Based Memory Models.

    PubMed

    Kato, Kenichi; Caplan, Jeremy B

    2017-02-13

    Convolution is a mathematical operation used in vector-models of memory that have been successful in explaining a broad range of behaviour, including memory for associations between pairs of items, an important primitive of memory upon which a broad range of everyday memory behaviour depends. However, convolution models have trouble with naturalistic item representations, which are highly auto-correlated (as one finds, e.g., with photographs), and this has cast doubt on their neural plausibility. Consequently, modellers working with convolution have used item representations composed of randomly drawn values, but introducing so-called noise-like representation raises the question how those random-like values might relate to actual item properties. We propose that a compromise solution to this problem may already exist. It has also long been known that the brain tends to reduce auto-correlations in its inputs. For example, centre-surround cells in the retina approximate a Difference-of-Gaussians (DoG) transform. This enhances edges, but also turns natural images into images that are closer to being statistically like white noise. We show the DoG-transformed images, although not optimal compared to noise-like representations, survive the convolution model better than naturalistic images. This is a proof-of-principle that the pervasive tendency of the brain to reduce auto-correlations may result in representations of information that are already adequately compatible with convolution, supporting the neural plausibility of convolution-based association-memory. (PsycINFO Database Record

  1. Fast vision through frameless event-based sensing and convolutional processing: application to texture recognition.

    PubMed

    Perez-Carrasco, Jose Antonio; Acha, Begona; Serrano, Carmen; Camunas-Mesa, Luis; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabe

    2010-04-01

    Address-event representation (AER) is an emergent hardware technology which shows a high potential for providing in the near future a solid technological substrate for emulating brain-like processing structures. When used for vision, AER sensors and processors are not restricted to capturing and processing still image frames, as in commercial frame-based video technology, but sense and process visual information in a pixel-level event-based frameless manner. As a result, vision processing is practically simultaneous to vision sensing, since there is no need to wait for sensing full frames. Also, only meaningful information is sensed, communicated, and processed. Of special interest for brain-like vision processing are some already reported AER convolutional chips, which have revealed a very high computational throughput as well as the possibility of assembling large convolutional neural networks in a modular fashion. It is expected that in a near future we may witness the appearance of large scale convolutional neural networks with hundreds or thousands of individual modules. In the meantime, some research is needed to investigate how to assemble and configure such large scale convolutional networks for specific applications. In this paper, we analyze AER spiking convolutional neural networks for texture recognition hardware applications. Based on the performance figures of already available individual AER convolution chips, we emulate large scale networks using a custom made event-based behavioral simulator. We have developed a new event-based processing architecture that emulates with AER hardware Manjunath's frame-based feature recognition software algorithm, and have analyzed its performance using our behavioral simulator. Recognition rate performance is not degraded. However, regarding speed, we show that recognition can be achieved before an equivalent frame is fully sensed and transmitted.

  2. Punctured Parallel and Serial Concatenated Convolutional Codes for BPSK/QPSK Channels

    NASA Technical Reports Server (NTRS)

    Acikel, Omer Fatih

    1999-01-01

    As available bandwidth for communication applications becomes scarce, bandwidth-efficient modulation and coding schemes become ever important. Since their discovery in 1993, turbo codes (parallel concatenated convolutional codes) have been the center of the attention in the coding community because of their bit error rate performance near the Shannon limit. Serial concatenated convolutional codes have also been shown to be as powerful as turbo codes. In this dissertation, we introduce algorithms for designing bandwidth-efficient rate r = k/(k + 1),k = 2, 3,..., 16, parallel and rate 3/4, 7/8, and 15/16 serial concatenated convolutional codes via puncturing for BPSK/QPSK (Binary Phase Shift Keying/Quadrature Phase Shift Keying) channels. Both parallel and serial concatenated convolutional codes have initially, steep bit error rate versus signal-to-noise ratio slope (called the -"cliff region"). However, this steep slope changes to a moderate slope with increasing signal-to-noise ratio, where the slope is characterized by the weight spectrum of the code. The region after the cliff region is called the "error rate floor" which dominates the behavior of these codes in moderate to high signal-to-noise ratios. Our goal is to design high rate parallel and serial concatenated convolutional codes while minimizing the error rate floor effect. The design algorithm includes an interleaver enhancement procedure and finds the polynomial sets (only for parallel concatenated convolutional codes) and the puncturing schemes that achieve the lowest bit error rate performance around the floor for the code rates of interest.

  3. Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

    NASA Astrophysics Data System (ADS)

    Acciarri, R.; Adams, C.; An, R.; Asaadi, J.; Auger, M.; Bagby, L.; Baller, B.; Barr, G.; Bass, M.; Bay, F.; Bishai, M.; Blake, A.; Bolton, T.; Bugel, L.; Camilleri, L.; Caratelli, D.; Carls, B.; Castillo Fernandez, R.; Cavanna, F.; Chen, H.; Church, E.; Cianci, D.; Collin, G. H.; Conrad, J. M.; Convery, M.; Crespo-Anadón, J. I.; Del Tutto, M.; Devitt, D.; Dytman, S.; Eberly, B.; Ereditato, A.; Escudero Sanchez, L.; Esquivel, J.; Fleming, B. T.; Foreman, W.; Furmanski, A. P.; Garvey, G. T.; Genty, V.; Goeldi, D.; Gollapinni, S.; Graf, N.; Gramellini, E.; Greenlee, H.; Grosso, R.; Guenette, R.; Hackenburg, A.; Hamilton, P.; Hen, O.; Hewes, J.; Hill, C.; Ho, J.; Horton-Smith, G.; James, C.; de Vries, J. Jan; Jen, C.-M.; Jiang, L.; Johnson, R. A.; Jones, B. J. P.; Joshi, J.; Jostlein, H.; Kaleko, D.; Karagiorgi, G.; Ketchum, W.; Kirby, B.; Kirby, M.; Kobilarcik, T.; Kreslo, I.; Laube, A.; Li, Y.; Lister, A.; Littlejohn, B. R.; Lockwitz, S.; Lorca, D.; Louis, W. C.; Luethi, M.; Lundberg, B.; Luo, X.; Marchionni, A.; Mariani, C.; Marshall, J.; Martinez Caicedo, D. A.; Meddage, V.; Miceli, T.; Mills, G. B.; Moon, J.; Mooney, M.; Moore, C. D.; Mousseau, J.; Murrells, R.; Naples, D.; Nienaber, P.; Nowak, J.; Palamara, O.; Paolone, V.; Papavassiliou, V.; Pate, S. F.; Pavlovic, Z.; Porzio, D.; Pulliam, G.; Qian, X.; Raaf, J. L.; Rafique, A.; Rochester, L.; von Rohr, C. Rudolf; Russell, B.; Schmitz, D. W.; Schukraft, A.; Seligman, W.; Shaevitz, M. H.; Sinclair, J.; Snider, E. L.; Soderberg, M.; Söldner-Rembold, S.; Soleti, S. R.; Spentzouris, P.; Spitz, J.; St. John, J.; Strauss, T.; Szelc, A. M.; Tagg, N.; Terao, K.; Thomson, M.; Toups, M.; Tsai, Y.-T.; Tufanli, S.; Usher, T.; Van de Water, R. G.; Viren, B.; Weber, M.; Weston, J.; Wickremasinghe, D. A.; Wolbers, S.; Wongjirad, T.; Woodruff, K.; Yang, T.; Zeller, G. P.; Zennamo, J.; Zhang, C.

    2017-03-01

    We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.

  4. Patient-specific dosimetry based on quantitative SPECT imaging and 3D-DFT convolution

    SciTech Connect

    Akabani, G.; Hawkins, W.G.; Eckblade, M.B.; Leichner, P.K.

    1999-01-01

    The objective of this study was to validate the use of a 3-D discrete Fourier Transform (3D-DFT) convolution method to carry out the dosimetry for I-131 for soft tissues in radioimmunotherapy procedures. To validate this convolution method, mathematical and physical phantoms were used as a basis of comparison with Monte Carlo transport (MCT) calculations which were carried out using the EGS4 system code. The mathematical phantom consisted of a sphere containing uniform and nonuniform activity distributions. The physical phantom consisted of a cylinder containing uniform and nonuniform activity distributions. Quantitative SPECT reconstruction was carried out using the Circular Harmonic Transform (CHT) algorithm.

  5. Blind separation of convolutive sEMG mixtures based on independent vector analysis

    NASA Astrophysics Data System (ADS)

    Wang, Xiaomei; Guo, Yina; Tian, Wenyan

    2015-12-01

    An independent vector analysis (IVA) method base on variable-step gradient algorithm is proposed in this paper. According to the sEMG physiological properties, the IVA model is applied to the frequency-domain separation of convolutive sEMG mixtures to extract motor unit action potentials information of sEMG signals. The decomposition capability of proposed method is compared to the one of independent component analysis (ICA), and experimental results show the variable-step gradient IVA method outperforms ICA in blind separation of convolutive sEMG mixtures.

  6. Implementation of large kernel 2-D convolution in limited FPGA resource

    NASA Astrophysics Data System (ADS)

    Zhong, Sheng; Li, Yang; Yan, Luxin; Zhang, Tianxu; Cao, Zhiguo

    2007-12-01

    2-D Convolution is a simple mathematical operation which is fundamental to many common image processing operators. Using FPGA to implement the convolver can greatly reduce the DSP's heavy burden in signal processing. But with the limit resource the FPGA can implement a convolver with small 2-D kernel. In this paper, An FIFO type line delayer is presented to serve as the data buffer for convolution to reduce the data fetching operation. A finite state machine is applied to control the reuse of multipliers and adders arrays. With these two techniques, a resource limited FPGA can be used to implement a larger kernel convolver which is commonly used in image process systems.

  7. Discrete singular convolution mapping methods for solving singular boundary value and boundary layer problems

    NASA Astrophysics Data System (ADS)

    Pindza, Edson; Maré, Eben

    2017-03-01

    A modified discrete singular convolution method is proposed. The method is based on the single (SE) and double (DE) exponential transformation to speed up the convergence of the existing methods. Numerical computations are performed on a wide variety of singular boundary value and singular perturbed problems in one and two dimensions. The obtained results from discrete singular convolution methods based on single and double exponential transformations are compared with each other, and with the existing methods too. Numerical results confirm that these methods are considerably efficient and accurate in solving singular and regular problems. Moreover, the method can be applied to a wide class of nonlinear partial differential equations.

  8. High-rate systematic recursive convolutional encoders: minimal trellis and code search

    NASA Astrophysics Data System (ADS)

    Benchimol, Isaac; Pimentel, Cecilio; Souza, Richard Demo; Uchôa-Filho, Bartolomeu F.

    2012-12-01

    We consider high-rate systematic recursive convolutional encoders to be adopted as constituent encoders in turbo schemes. Douillard and Berrou showed that, despite its complexity, the construction of high-rate turbo codes by means of high-rate constituent encoders is advantageous over the construction based on puncturing rate-1/2 constituent encoders. To reduce the decoding complexity of high-rate codes, we introduce the construction of the minimal trellis for a systematic recursive convolutional encoding matrix. A code search is conducted and examples are provided which indicate that a more finely grained decoding complexity-error performance trade-off is obtained.

  9. Fast computation algorithm for the Rayleigh-Sommerfeld diffraction formula using a type of scaled convolution.

    PubMed

    Nascov, Victor; Logofătu, Petre Cătălin

    2009-08-01

    We describe a fast computational algorithm able to evaluate the Rayleigh-Sommerfeld diffraction formula, based on a special formulation of the convolution theorem and the fast Fourier transform. What is new in our approach compared to other algorithms is the use of a more general type of convolution with a scale parameter, which allows for independent sampling intervals in the input and output computation windows. Comparison between the calculations made using our algorithm and direct numeric integration show a very good agreement, while the computation speed is increased by orders of magnitude.

  10. Fabrication of tungsten wire reinforced nickel-base alloy composites

    NASA Technical Reports Server (NTRS)

    Brentnall, W. D.; Toth, I. J.

    1974-01-01

    Fabrication methods for tungsten fiber reinforced nickel-base superalloy composites were investigated. Three matrix alloys in pre-alloyed powder or rolled sheet form were evaluated in terms of fabricability into composite monotape and multi-ply forms. The utility of monotapes for fabricating more complex shapes was demonstrated. Preliminary 1093C (2000F) stress rupture tests indicated that efficient utilization of fiber strength was achieved in composites fabricated by diffusion bonding processes. The fabrication of thermal fatigue specimens is also described.

  11. A Fast Numerical Method for Max-Convolution and the Application to Efficient Max-Product Inference in Bayesian Networks.

    PubMed

    Serang, Oliver

    2015-08-01

    Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to max-product inference is the max-convolution problem (sometimes presented in log-transformed form and denoted as "infimal convolution," "min-convolution," or "convolution on the tropical semiring"), for which no O(k log(k)) method is currently known. Presented here is an O(k log(k)) numerical method for estimating the max-convolution of two nonnegative vectors (e.g., two probability mass functions), where k is the length of the larger vector. This numerical max-convolution method is then demonstrated by performing fast max-product inference on a convolution tree, a data structure for performing fast inference given information on the sum of n discrete random variables in O(nk log(nk)log(n)) steps (where each random variable has an arbitrary prior distribution on k contiguous possible states). The numerical max-convolution method can be applied to specialized classes of hidden Markov models to reduce the runtime of computing the Viterbi path from nk(2) to nk log(k), and has potential application to the all-pairs shortest paths problem.

  12. Determining micro- and macro- geometry of fabric and fabric reinforced composites

    NASA Astrophysics Data System (ADS)

    Huang, Lejian

    Textile composites are made from textile fabric and resin. Depending on the weaving pattern, composite reinforcements can be characterized into two groups: uniform fabric and near-net shape fabric. Uniform fabric can be treated as an assembly of its smallest repeating pattern also called a unit cell; the latter is a single component with complex structure. Due to advantages of cost savings and inherent toughness, near-net shape fabric has gained great success in composite industries, for application such as turbine blades. Mechanical properties of textile composites are mainly determined by the geometry of the composite reinforcements. The study of a composite needs a computational tool to link fabric micro- and macro-geometry with the textile weaving process and composite manufacturing process. A textile fabric consists of a number of yarns or tows, and each yarn is a bundle of fibers. In this research, a fiber-level approach known as the digital element approach (DEA) is adopted to model the micro- and macro-geometry of fabric and fabric reinforced composites. This approach determines fabric geometry based on textile weaving mechanics. A solver with a dynamic explicit algorithm is employed in the DEA. In modeling a uniform fabric, the topology of the fabric unit cell is first established based on the weaving pattern, followed by yarn discretization. An explicit algorithm with a periodic boundary condition is then employed during the simulation. After its detailed geometry is obtained, the unit cell is then assembled to yield a fabric micro-geometry. Fabric micro-geometry can be expressed at both fiber- and yarn-levels. In modeling a near-net shape fabric component, all theories used in simulating the uniform fabric are kept except the periodic boundary condition. Since simulating the entire component at the fiber-level requires a large amount of time and memory, parallel program is used during the simulation. In modeling a net-shape composite, a dynamic molding

  13. Fabrication Technology

    SciTech Connect

    Blaedel, K.L.

    1993-03-01

    The mission of the Fabrication Technology thrust area is to have an adequate base of manufacturing technology, not necessarily resident at Lawrence Livermore National Laboratory (LLNL), to conduct the future business of LLNL. The specific goals continue to be to (1) develop an understanding of fundamental fabrication processes; (2) construct general purpose process models that will have wide applicability; (3) document findings and models in journals; (4) transfer technology to LLNL programs, industry, and colleagues; and (5) develop continuing relationships with the industrial and academic communities to advance the collective understanding of fabrication processes. The strategy to ensure success is changing. For technologies in which they are expert and which will continue to be of future importance to LLNL, they can often attract outside resources both to maintain their expertise by applying it to a specific problem and to help fund further development. A popular vehicle to fund such work is the Cooperative Research and Development Agreement with industry. For technologies needing development because of their future critical importance and in which they are not expert, they use internal funding sources. These latter are the topics of the thrust area. Three FY-92 funded projects are discussed in this section. Each project clearly moves the Fabrication Technology thrust area towards the goals outlined above. They have also continued their membership in the North Carolina State University Precision Engineering Center, a multidisciplinary research and graduate program established to provide the new technologies needed by high-technology institutions in the US. As members, they have access to and use of the results of their research projects, many of which parallel the precision engineering efforts at LLNL.

  14. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

    PubMed Central

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-01-01

    Abstract. Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the

  15. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

    NASA Astrophysics Data System (ADS)

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-03-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by

  16. Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by Refining SCIRD-TS Filter Banks.

    PubMed

    Annunziata, Roberto; Trucco, Emanuele

    2016-11-01

    Deep learning has shown great potential for curvilinear structure (e.g., retinal blood vessels and neurites) segmentation as demonstrated by a recent auto-context regression architecture based on filter banks learned by convolutional sparse coding. However, learning such filter banks is very time-consuming, thus limiting the amount of filters employed and the adaptation to other data sets (i.e., slow re-training). We address this limitation by proposing a novel acceleration strategy to speed-up convolutional sparse coding filter learning for curvilinear structure segmentation. Our approach is based on a novel initialisation strategy (warm start), and therefore it is different from recent methods improving the optimisation itself. Our warm-start strategy is based on carefully designed hand-crafted filters (SCIRD-TS), modelling appearance properties of curvilinear structures which are then refined by convolutional sparse coding. Experiments on four diverse data sets, including retinal blood vessels and neurites, suggest that the proposed method reduces significantly the time taken to learn convolutional filter banks (i.e., up to -82%) compared to conventional initialisation strategies. Remarkably, this speed-up does not worsen performance; in fact, filters learned with the proposed strategy often achieve a much lower reconstruction error and match or exceed the segmentation performance of random and DCT-based initialisation, when used as input to a random forest classifier.

  17. The venom apparatus in stenogastrine wasps: subcellular features of the convoluted gland.

    PubMed

    Petrocelli, Iacopo; Turillazzi, Stefano; Delfino, Giovanni

    2014-09-01

    In the wasp venom apparatus, the convoluted gland is the tract of the thin secretory unit, i.e. filament, contained in the muscular reservoir. Previous transmission electron microscope investigation on Stenogastrinae disclosed that the free filaments consist of distal and proximal tracts, from/to the venom reservoir, characterized by class 3 and 2 gland patterns, respectively. This study aims to extend the ultrastructural analysis to the convoluted tract, in order to provide a thorough, subcellular representation of the venom gland in these Asian wasps. Our findings showed that the convoluted gland is a continuation of the proximal tract, with secretory cells provided with a peculiar apical invagination, the extracellular cavity, collecting their products. This compartment holds a simple end-apparatus lined by large and ramified microvilli that contribute to the processing of the secretory product. A comparison between previous and present findings reveals a noticeable regionalization of the stenogastrine venom filaments and suggests that the secretory product acquires its ultimate composition in the convoluted tract.

  18. A multilevel local discrete convolution method for the numerical solution for Maxwell's Equations

    NASA Astrophysics Data System (ADS)

    Lo, Boris; Colella, Phillip

    2016-10-01

    We present a new discrete multilevel local discrete convolution method for solving Maxwell's equations in three dimensions. We obtain an explicit real-space representation for the propagator of an auxiliary system of differential equations with initial value constraints that is equivalent to Maxwell's equations. The propagator preserves finite speed of propagation and source locality. Because the propagator involves convolution against a singular distribution, we regularize via convolution with smoothing kernels (B-splines) prior to sampling. We have shown that the ultimate discrete convolutional propagator can be constructed to attain an arbitrarily high order of accuracy by using higher-order regularizing kernels and finite difference stencils. The discretized propagator is compactly supported and can be applied using Hockney's method (1970) and parallelized using domain decomposition, leading to a method that is computationally efficient. The algorithm is extended to work for locally refined fixed hierarchy of rectangular grids. This research is supported by the Office of Advanced Scientific Computing Research of the US Department of Energy under Contract Number DE-AC02-05CH11231.

  19. Digital Tomosynthesis System Geometry Analysis Using Convolution-Based Blur-and-Add (BAA) Model.

    PubMed

    Wu, Meng; Yoon, Sungwon; Solomon, Edward G; Star-Lack, Josh; Pelc, Norbert; Fahrig, Rebecca

    2016-01-01

    Digital tomosynthesis is a three-dimensional imaging technique with a lower radiation dose than computed tomography (CT). Due to the missing data in tomosynthesis systems, out-of-plane structures in the depth direction cannot be completely removed by the reconstruction algorithms. In this work, we analyzed the impulse responses of common tomosynthesis systems on a plane-to-plane basis and proposed a fast and accurate convolution-based blur-and-add (BAA) model to simulate the backprojected images. In addition, the analysis formalism describing the impulse response of out-of-plane structures can be generalized to both rotating and parallel gantries. We implemented a ray tracing forward projection and backprojection (ray-based model) algorithm and the convolution-based BAA model to simulate the shift-and-add (backproject) tomosynthesis reconstructions. The convolution-based BAA model with proper geometry distortion correction provides reasonably accurate estimates of the tomosynthesis reconstruction. A numerical comparison indicates that the simulated images using the two models differ by less than 6% in terms of the root-mean-squared error. This convolution-based BAA model can be used in efficient system geometry analysis, reconstruction algorithm design, out-of-plane artifacts suppression, and CT-tomosynthesis registration.

  20. The VLSI design of error-trellis syndrome decoding for convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Jensen, J. M.; Truong, T. K.; Hsu, I. S.

    1985-01-01

    A recursive algorithm using the error-trellis decoding technique is developed to decode convolutional codes (CCs). An example, illustrating the very large scale integration (VLSI) architecture of such a decode, is given for a dual-K CC. It is demonstrated that such a decoder can be realized readily on a single chip with metal-nitride-oxide-semiconductor technology.

  1. The VLSI design of an error-trellis syndrome decoder for certain convolutional codes

    NASA Technical Reports Server (NTRS)

    Reed, I. S.; Jensen, J. M.; Hsu, I.-S.; Truong, T. K.

    1986-01-01

    A recursive algorithm using the error-trellis decoding technique is developed to decode convolutional codes (CCs). An example, illustrating the very large scale integration (VLSI) architecture of such a decode, is given for a dual-K CC. It is demonstrated that such a decoder can be realized readily on a single chip with metal-nitride-oxide-semiconductor technology.

  2. Linear diffusion-wave channel routing using a discrete Hayami convolution method

    NASA Astrophysics Data System (ADS)

    Wang, Li; Wu, Joan Q.; Elliot, William J.; Fiedler, Fritz R.; Lapin, Sergey

    2014-02-01

    The convolution of an input with a response function has been widely used in hydrology as a means to solve various problems analytically. Due to the high computation demand in solving the functions using numerical integration, it is often advantageous to use the discrete convolution instead of the integration of the continuous functions. This approach greatly reduces the amount of the computational work; however, it increases the possibility for mass balance errors. In this study, we analyzed the characteristics of the kernel function for the Hayami convolution solution to the linear diffusion-wave channel routing with distributed lateral inflow. We propose two ways of selection of the discrete kernel function values: using the exact point values or using the center-averaged values. Through a hypothetical example and the applications to Asotin Creek, WA and the Clearwater River, ID, we showed that when the point kernel function values were used in the discrete Hayami convolution (DHC) solution, the mass balance error of channel routing is dependent on the number of time steps on the rising limb of the Hayami kernel function. The mass balance error is negligible when there are more than 1.8 time steps on the rising limb of the kernel function. The fewer time steps on the rising limb, the greater risk of high mass balance errors. When the average kernel function values are used for the DHC solution, however, the mass balance is always maintained, since the integration of the discrete kernel function is always unity.

  3. Convolutional FEC design considerations for data transmission over PSK satellite channels

    NASA Astrophysics Data System (ADS)

    Garrison, G. J.; Wong, V. C.

    Simulation results are provided for rate R = 1/2 convolutional error correcting codes suited to data transmission over BPSK, gray coded QPSK, and OQPSK channels. The burst generation mechanism resulting from differential encoding/decoding is analyzed in terms of the impairment to code performance and offsetting internal/external interleaving techniques are described.

  4. On the twisted convolution product and the Weyl transformation of tempered distributions

    NASA Astrophysics Data System (ADS)

    Maillard, J. M.

    It is well known that the Weyl transformation in a phase space R21, transforms the elements of L( R21) in trace class operators and the elements of L 2( R21) in the Hilbert-Schmidt operators of the Hilbert space L 2( R1); this fact leads to a general method of quantization suggested by E. Wigner and J.E. Moyal and developed by M. Flato, A. Lichnerowicz, C. Fronsdal, D. Sternheimer and F. Bayen for an arbitrary symplectic manifold, known under the name of star-product method. In this context, it is important to study the Weyl transforms of the tempered distributions on the phase space and that of the star-exponentials which gave the spectrum in this process of quantization. We analyze here the relations between the star-product, the twisted convolution product and the Weyl transformation of tempered distributions. We introduce symplectic differential operators which permit us to study the structure of the space O1λ λ ≠ 0, (similar to the space O1C) of the left (twisted) convolution operators of L( R21) which permit to define the twisted convolution product in the space L( R21), and the structures of the admissible symbols for the Weyl transformation (i.e. the domain of the Weyl transformation). We prove that the bounded operators of L 2( R1) are exactly the Weyl transforms of the bounded (twisted) convolution operators of L 2( R21). We give an expression of the integral formula of the star product in terms of twisted convolution products which is valid in the most general case. The unitary representations of the Heisenberg group play an important role here.

  5. Parametric phase information based 2D Cepstrum PSF estimation method for blind de-convolution of ultrasound imaging

    NASA Astrophysics Data System (ADS)

    Kang, Jooyoung; Park, Sung-Chan; Kim, Jung-ho; Song, Jongkeun

    2014-02-01

    In the ultrasound imaging system, blurring which occurs after passing through ultrasound scanner system, represents point spread function (PSF) that describes the response of the ultrasound imaging system to a point source distribution. So, de-blurring can be achieved by de-convolving the ultrasound images with an estimated of corresponding PSF. However, it is hard to attain an accurate estimation of PSF due to the unknown properties of the tissues of the human body through the ultrasound signal propagates. In this paper, we present a new method for PSF estimation in the Fourier domain (FD) based on parametric minimum phase information, and simultaneously, it performs fast 2D de-convolution in the ultrasound imaging system. Although most of complex cepstrum methods [14], are obtained using complex 2D phase unwrapping [18] [19] in order to estimate the FD-phase information of PSF, our algorithm estimates the 2D PSF using 2D FD-phase information with the parametric weighting factor α and β. They affect the feature of PSF shapes.This makes the computations much simpler and the estimation more accurate. Our algorithm works on the beam-formed uncompressed radio-frequency data, with pre-measured and estimated 2D PSFs database from actual probe used. We have tested our algorithm with vera-sonic system and commercial ultrasound scanner (Philips C4-2), in known speed of sound phantoms and unknown speeds in vivo scans.

  6. Triaxial Fabrics

    NASA Technical Reports Server (NTRS)

    1981-01-01

    Gentax Corporation's triaxal fabrics are woven from three separate yarn sets whose intersections form equilateral triangles. This type of weave, derived from space shuttle pressure suits, assures practically equal strength in every direction; has essentially no bias, or weak dimension offering greater resistance to tear and shear along with significant weight reduction. Applications of the Triax line include inflatable equipment, life vests, aircraft evacuation slides, helicopter flotation devices, tension structures, safety clothing and sailcloth for boats. Ability to accept compound curvatures with no distortion of the weave configuration makes it useful in manufacturing molded composites.

  7. Fabricated Elastin.

    PubMed

    Yeo, Giselle C; Aghaei-Ghareh-Bolagh, Behnaz; Brackenreg, Edwin P; Hiob, Matti A; Lee, Pearl; Weiss, Anthony S

    2015-11-18

    The mechanical stability, elasticity, inherent bioactivity, and self-assembly properties of elastin make it a highly attractive candidate for the fabrication of versatile biomaterials. The ability to engineer specific peptide sequences derived from elastin allows the precise control of these physicochemical and organizational characteristics, and further broadens the diversity of elastin-based applications. Elastin and elastin-like peptides can also be modified or blended with other natural or synthetic moieties, including peptides, proteins, polysaccharides, and polymers, to augment existing capabilities or confer additional architectural and biofunctional features to compositionally pure materials. Elastin and elastin-based composites have been subjected to diverse fabrication processes, including heating, electrospinning, wet spinning, solvent casting, freeze-drying, and cross-linking, for the manufacture of particles, fibers, gels, tubes, sheets and films. The resulting materials can be tailored to possess specific strength, elasticity, morphology, topography, porosity, wettability, surface charge, and bioactivity. This extraordinary tunability of elastin-based constructs enables their use in a range of biomedical and tissue engineering applications such as targeted drug delivery, cell encapsulation, vascular repair, nerve regeneration, wound healing, and dermal, cartilage, bone, and dental replacement.

  8. Fabricated elastin

    PubMed Central

    Yeo, Giselle C.; Weiss, Anthony S.

    2015-01-01

    The mechanical stability, elasticity, inherent bioactivity, and self-assembly properties of elastin make it a highly attractive candidate for the fabrication of versatile biomaterials. The ability to engineer specific peptide sequences derived from elastin allows for precise control of these physicochemical and organizational characteristics, and further broadens the diversity of elastin-based applications. Elastin and elastin-like peptides can also be modified or blended with other natural or synthetic moieties, including peptides, proteins, polysaccharides and polymers, to augment existing capabilities or confer additional architectural and biofunctional features to compositionally pure materials. Elastin and elastin-based composites have been subjected to diverse fabrication processes, including heating, electrospinning, wet spinning, solvent casting, freeze-drying, and cross-linking, for the manufacture of particles, fibers, gels, tubes, sheets and films. The resulting materials can be tailored to possess specific strength, elasticity, morphology, topography, porosity, wettability, surface charge and bioactivity. This extraordinary tunability of elastin-based constructs enables their use in a range of biomedical and tissue engineering applications such as targeted drug delivery, cell encapsulation, vascular repair, nerve regeneration, wound healing, and dermal, cartilage, bone and dental replacement. PMID:25771993

  9. Post polymerization cure shape memory polymers

    DOEpatents

    Wilson, Thomas S; Hearon, Michael Keith; Bearinger, Jane P

    2014-11-11

    This invention relates to chemical polymer compositions, methods of synthesis, and fabrication methods for devices regarding polymers capable of displaying shape memory behavior (SMPs) and which can first be polymerized to a linear or branched polymeric structure, having thermoplastic properties, subsequently processed into a device through processes typical of polymer melts, solutions, and dispersions and then crossed linked to a shape memory thermoset polymer retaining the processed shape.

  10. Post polymerization cure shape memory polymers

    DOEpatents

    Wilson, Thomas S.; Hearon, II, Michael Keith; Bearinger, Jane P.

    2017-01-10

    This invention relates to chemical polymer compositions, methods of synthesis, and fabrication methods for devices regarding polymers capable of displaying shape memory behavior (SMPs) and which can first be polymerized to a linear or branched polymeric structure, having thermoplastic properties, subsequently processed into a device through processes typical of polymer melts, solutions, and dispersions and then crossed linked to a shape memory thermoset polymer retaining the processed shape.

  11. A Cable-Shaped Lithium Sulfur Battery.

    PubMed

    Fang, Xin; Weng, Wei; Ren, Jing; Peng, Huisheng

    2016-01-20

    A carbon nanostructured hybrid fiber is developed by integrating mesoporous carbon and graphene oxide into aligned carbon nanotubes. This hybrid fiber is used as a 1D cathode to fabricate a new cable-shaped lithium-sulfur battery. The fiber cathode exhibits a decent specific capacity and lifespan, which makes the cable-shaped lithium-sulfur battery rank far ahead of other fiber-shaped batteries.

  12. Lexan Linear Shaped Charge Holder with Magnets and Backing Plate

    NASA Technical Reports Server (NTRS)

    Maples, Matthew W.; Dutton, Maureen L.; Hacker, Scott C.; Dean, Richard J.; Kidd, Nicholas; Long, Chris; Hicks, Robert C.

    2013-01-01

    A method was developed for cutting a fabric structural member in an inflatable module, without damaging the internal structure of the module, using linear shaped charge. Lexan and magnets are used in a charge holder to precisely position the linear shaped charge over the desired cut area. Two types of charge holders have been designed, each with its own backing plate. One holder cuts fabric straps in the vertical configuration, and the other charge holder cuts fabric straps in the horizontal configuration.

  13. Brain tumor grading based on Neural Networks and Convolutional Neural Networks.

    PubMed

    Yuehao Pan; Weimin Huang; Zhiping Lin; Wanzheng Zhu; Jiayin Zhou; Wong, Jocelyn; Zhongxiang Ding

    2015-08-01

    This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, with some combination operations between multiphase MRIs. Compared to other researches, which involve additional effort to design and choose feature sets, the approach used in this paper leverages the learning capability of deep learning machine. We present the grading performance on the testing data measured by the sensitivity and specificity. The results show a maximum improvement of 18% on grading performance of Convolutional Neural Networks based on sensitivity and specificity compared to Neural Networks. We also visualize the kernels trained in different layers and display some self-learned features obtained from Convolutional Neural Networks.

  14. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.

    PubMed

    Li, Wei; Cao, Peng; Zhao, Dazhe; Wang, Junbo

    2016-01-01

    Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods.

  15. Video-based convolutional neural networks for activity recognition from robot-centric videos

    NASA Astrophysics Data System (ADS)

    Ryoo, M. S.; Matthies, Larry

    2016-05-01

    In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

  16. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images

    PubMed Central

    Li, Wei; Zhao, Dazhe; Wang, Junbo

    2016-01-01

    Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of autolearning representation and strong generalization ability. A specified network structure for nodule images is proposed to solve the recognition of three types of nodules, that is, solid, semisolid, and ground glass opacity (GGO). Deep convolutional neural networks are trained by 62,492 regions-of-interest (ROIs) samples including 40,772 nodules and 21,720 nonnodules from the Lung Image Database Consortium (LIDC) database. Experimental results demonstrate the effectiveness of the proposed method in terms of sensitivity and overall accuracy and that it consistently outperforms the competing methods. PMID:28070212

  17. Shape-Morphing Nanocomposite Origami

    PubMed Central

    2015-01-01

    Nature provides a vast array of solid materials that repeatedly and reversibly transform in shape in response to environmental variations. This property is essential, for example, for new energy-saving technologies, efficient collection of solar radiation, and thermal management. Here we report a similar shape-morphing mechanism using differential swelling of hydrophilic polyelectrolyte multilayer inkjets deposited on an LBL carbon nanotube (CNT) composite. The out-of-plane deflection can be precisely controlled, as predicted by theoretical analysis. We also demonstrate a controlled and stimuli-responsive twisting motion on a spiral-shaped LBL nanocomposite. By mimicking the motions achieved in nature, this method offers new opportunities for the design and fabrication of functional stimuli-responsive shape-morphing nanoscale and microscale structures for a variety of applications. PMID:24689908

  18. Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

    NASA Astrophysics Data System (ADS)

    Popko, E. A.; Weinstein, I. A.

    2016-08-01

    Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

  19. Transient electromagnetic modeling of the ZR accelerator water convolute and stack.

    SciTech Connect

    Lehr, Jane Marie; Elizondo-Decanini, Juan Manuel; Turner, C. David; Coats, Rebecca Sue; Bohnhoff, William J.; Pointon, Timothy David; Pasik, Michael Francis; Johnson, William Arthur; Savage, Mark Edward

    2005-06-01

    The ZR accelerator is a refurbishment of Sandia National Laboratories Z accelerator [1]. The ZR accelerator components were designed using electrostatic and circuit modeling tools. Transient electromagnetic modeling has played a complementary role in the analysis of ZR components [2]. In this paper we describe a 3D transient electromagnetic analysis of the ZR water convolute and stack using edge-based finite element techniques.

  20. Proteomic analysis of brush-border membrane vesicles isolated from purified proximal convoluted tubules

    PubMed Central

    Walmsley, Scott J.; Broeckling, Corey; Hess, Ann; Prenni, Jessica

    2010-01-01

    The renal proximal convoluted tubule is the primary site of water, electrolyte and nutrient reabsorption and of active secretion of selected molecules. Proteins in the apical brush-border membrane facilitate these functions and initiate some of the cellular responses to altered renal physiology. The current study uses two-dimensional liquid chromatography/mass spectrometry to compare brush border membrane vesicles isolated from rat renal cortex (BBMVCTX) and from purified proximal convoluted tubules (BBMVPCT). Both proteomic data and Western blot analysis indicate that the BBMVCTX contain apical membrane proteins from cortical cells other than the proximal tubule. This heterogeneity was greatly reduced in the BBMVPCT. Proteomic analysis identified 193 proteins common to both samples, 21 proteins unique to BBMVCTX, and 57 proteins unique to BBMVPCT. Spectral counts were used to quantify relative differences in protein abundance. This analysis identified 42 and 50 proteins that are significantly enriched (p values ≤0.001) in the BBMVCTX and BBMVPCT, respectively. These data were validated by measurement of γ-glutamyltranspeptidase activity and by Western blot analysis. The combined results establish that BBMVPCT are primarily derived from the proximal convoluted tubule (S1 and S2 segments), whereas BBMVCTX include proteins from the proximal straight tubule (S3 segment). Analysis of functional annotations indicated that BBMVPCT are enriched in mitochondrial proteins and enzymes involved in glucose and organic acid metabolism. Thus the current study reports a detailed proteomic analysis of the brush-border membrane of the rat renal proximal convoluted tubule and provides a database for future hypothesis-driven research. PMID:20219825

  1. Convolution-based estimation of organ dose in tube current modulated CT

    NASA Astrophysics Data System (ADS)

    Tian, Xiaoyu; Segars, W. P.; Dixon, R. L.; Samei, Ehsan

    2015-03-01

    Among the various metrics that quantify radiation dose in computed tomography (CT), organ dose is one of the most representative quantities reflecting patient-specific radiation burden.1 Accurate estimation of organ dose requires one to effectively model the patient anatomy and the irradiation field. As illustrated in previous studies, the patient anatomy factor can be modeled using a library of computational phantoms with representative body habitus.2 However, the modeling of irradiation field can be practically challenging, especially for CT exams performed with tube current modulation. The central challenge is to effectively quantify the scatter irradiation field created by the dynamic change of tube current. In this study, we present a convolution-based technique to effectively quantify the primary and scatter irradiation field for TCM examinations. The organ dose for a given clinical patient can then be rapidly determined using the convolution-based method, a patient-matching technique, and a library of computational phantoms. 58 adult patients were included in this study (age range: 18-70 y.o., weight range: 60-180 kg). One computational phantom was created based on the clinical images of each patient. Each patient was optimally matched against one of the remaining 57 computational phantoms using a leave-one-out strategy. For each computational phantom, the organ dose coefficients (CTDIvol-normalized organ dose) under fixed tube current were simulated using a validated Monte Carlo simulation program. Such organ dose coefficients were multiplied by a scaling factor, (CTDIvol )organ, convolution that quantifies the regional irradiation field. The convolution-based organ dose was compared with the organ dose simulated from Monte Carlo program with TCM profiles explicitly modeled on the original phantom created based on patient images. The estimation error was within 10% across all organs and modulation profiles for abdominopelvic examination. This strategy

  2. Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images.

    PubMed

    Cheng, Phillip M; Malhi, Harshawn S

    2017-04-01

    The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized into 11 categories based on the text annotation specified by the technologist for the image. Cropped images were rescaled to 256 × 256 resolution and randomized, with 4094 images from 136 studies constituting the training set, and 1423 images from 49 studies constituting the test set. The fully connected layers of two convolutional neural networks based on CaffeNet and VGGNet, previously trained on the 2012 Large Scale Visual Recognition Challenge data set, were retrained on the training set. Weights in the convolutional layers of each network were frozen to serve as fixed feature extractors. Accuracy on the test set was evaluated for each network. A radiologist experienced in abdominal ultrasound also independently classified the images in the test set into the same 11 categories. The CaffeNet network classified 77.3% of the test set images accurately (1100/1423 images), with a top-2 accuracy of 90.4% (1287/1423 images). The larger VGGNet network classified 77.9% of the test set accurately (1109/1423 images), with a top-2 accuracy of VGGNet was 89.7% (1276/1423 images). The radiologist classified 71.7% of the test set images correctly (1020/1423 images). The differences in classification accuracies between both neural networks and the radiologist were statistically significant (p < 0.001). The results demonstrate that transfer learning with convolutional neural networks may be used to construct effective classifiers for abdominal ultrasound images.

  3. A filtering approach based on Gaussian-powerlaw convolutions for local PET verification of proton radiotherapy.

    PubMed

    Parodi, Katia; Bortfeld, Thomas

    2006-04-21

    Because proton beams activate positron emitters in patients, positron emission tomography (PET) has the potential to play a unique role in the in vivo verification of proton radiotherapy. Unfortunately, the PET image is not directly proportional to the delivered radiation dose distribution. Current treatment verification strategies using PET therefore compare the actual PET image with full-blown Monte Carlo simulations of the PET signal. In this paper, we describe a simpler and more direct way to reconstruct the expected PET signal from the local radiation dose distribution near the distal fall-off region, which is calculated by the treatment planning programme. Under reasonable assumptions, the PET image can be described as a convolution of the dose distribution with a filter function. We develop a formalism to derive the filter function analytically. The main concept is the introduction of 'Q' functions defined as the convolution of a Gaussian with a powerlaw function. Special Q functions are the Gaussian itself and the error function. The convolution of two Q functions is another Q function. By fitting elementary dose distributions and their corresponding PET signals with Q functions, we derive the Q function approximation of the filter. The new filtering method has been validated through comparisons with Monte Carlo calculations and, in one case, with measured data. While the basic concept is developed under idealized conditions assuming that the absorbing medium is homogeneous near the distal fall-off region, a generalization to inhomogeneous situations is also described. As a result, the method can determine the distal fall-off region of the PET signal, and consequently the range of the proton beam, with millimetre accuracy. Quantification of the produced activity is possible. In conclusion, the PET activity resulting from a proton beam treatment can be determined by locally filtering the dose distribution as obtained from the treatment planning system. The

  4. Quantum Fields Obtained from Convoluted Generalized White Noise Never Have Positive Metric

    NASA Astrophysics Data System (ADS)

    Albeverio, Sergio; Gottschalk, Hanno

    2016-05-01

    It is proven that the relativistic quantum fields obtained from analytic continuation of convoluted generalized (Lévy type) noise fields have positive metric, if and only if the noise is Gaussian. This follows as an easy observation from a criterion by Baumann, based on the Dell'Antonio-Robinson-Greenberg theorem, for a relativistic quantum field in positive metric to be a free field.

  5. Convolutional Neural Network on Embedded Linux System-on-Chip: A Methodology and Performance Benchmark

    DTIC Science & Technology

    2016-05-01

    heat sink. Note that a final system could be made much smaller than this development board, which has “wasted” space compared to a board used in a...TECHNICAL REPORT 3010 May 2016 Convolutional Neural Network on Embedded Linux® System -on-Chip A Methodology and Performance Benchmark Daniel...in this report was performed by the IO Support to National Security Branch (Code 56120), the Mission Systems Engineering Branch (Code 56170), and the

  6. Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields.

    PubMed

    Liu, Fayao; Shen, Chunhua; Lin, Guosheng; Reid, Ian

    2016-10-01

    In this article, we tackle the problem of depth estimation from single monocular images. Compared with depth estimation using multiple images such as stereo depth perception, depth from monocular images is much more challenging. Prior work typically focuses on exploiting geometric priors or additional sources of information, most using hand-crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) set new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimation can be naturally formulated as a continuous conditional random field (CRF) learning problem. Therefore, here we present a deep convolutional neural field model for estimating depths from single monocular images, aiming to jointly explore the capacity of deep CNN and continuous CRF. In particular, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. We then further propose an equally effective model based on fully convolutional networks and a novel superpixel pooling method, which is about 10 times faster, to speedup the patch-wise convolutions in the deep model. With this more efficient model, we are able to design deeper networks to pursue better performance. Our proposed method can be used for depth estimation of general scenes with no geometric priors nor any extra information injected. In our case, the integral of the partition function can be calculated in a closed form such that we can exactly solve the log-likelihood maximization. Moreover, solving the inference problem for predicting depths of a test image is highly efficient as closed-form solutions exist. Experiments on both indoor and outdoor scene datasets demonstrate that the proposed method outperforms state-of-the-art depth estimation approaches.

  7. Nozzle fabrication technique

    NASA Technical Reports Server (NTRS)

    Wells, Dennis L. (Inventor)

    1988-01-01

    This invention relates to techniques for fabricating hour glass throat or convergent divergent nozzle shapes, and more particularly to new and improved techniques for forming rocket nozzles from electrically conductive material and forming cooling channels in the wall thereof. The concept of positioning a block of electrically conductive material so that its axis is set at a predetermined skew angle with relation to a travelling electron discharge machine electrode and thereafter revolving the body about its own axis to generate a hyperbolic surface of revolution, either internal or external is novel. The method will generate a rocket nozzle which may be provided with cooling channels using the same control and positioning system. The configuration of the cooling channels so produced are unique and novel. Also the method is adaptable to nonmetallic material using analogous cutting tools, such as, water jet, laser, abrasive wire and hot wire.

  8. Vehicle detection based on visual saliency and deep sparse convolution hierarchical model

    NASA Astrophysics Data System (ADS)

    Cai, Yingfeng; Wang, Hai; Chen, Xiaobo; Gao, Li; Chen, Long

    2016-07-01

    Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification. These types of methods generally have high processing times and low vehicle detection performance. To address this issue, a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed. A visual saliency calculation is firstly used to generate a small vehicle candidate area. The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection. The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group, which outperforms the existing state-of-the-art algorithms. More importantly, high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

  9. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

    PubMed

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-03-22

    Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture.

  10. Convolutional neural network approach for buried target recognition in FL-LWIR imagery

    NASA Astrophysics Data System (ADS)

    Stone, K.; Keller, J. M.

    2014-05-01

    A convolutional neural network (CNN) approach to recognition of buried explosive hazards in forward-looking long-wave infrared (FL-LWIR) imagery is presented. The convolutional filters in the first layer of the network are learned in the frequency domain, making enforcement of zero-phase and zero-dc response characteristics much easier. The spatial domain representations of the filters are forced to have unit l2 norm, and penalty terms are added to the online gradient descent update to encourage orthonormality among the convolutional filters, as well smooth first and second order derivatives in the spatial domain. The impact of these modifications on the generalization performance of the CNN model is investigated. The CNN approach is compared to a second recognition algorithm utilizing shearlet and log-gabor decomposition of the image coupled with cell-structured feature extraction and support vector machine classification. Results are presented for multiple FL-LWIR data sets recently collected from US Army test sites. These data sets include vehicle position information allowing accurate transformation between image and world coordinates and realistic evaluation of detection and false alarm rates.

  11. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

    PubMed Central

    Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

  12. Using hybrid GPU/CPU kernel splitting to accelerate spherical convolutions

    NASA Astrophysics Data System (ADS)

    Sutter, P. M.; Wandelt, B. D.; Elsner, F.

    2015-06-01

    We present a general method for accelerating by more than an order of magnitude the convolution of pixelated functions on the sphere with a radially-symmetric kernel. Our method splits the kernel into a compact real-space component and a compact spherical harmonic space component. These components can then be convolved in parallel using an inexpensive commodity GPU and a CPU. We provide models for the computational cost of both real-space and Fourier space convolutions and an estimate for the approximation error. Using these models we can determine the optimum split that minimizes the wall clock time for the convolution while satisfying the desired error bounds. We apply this technique to the problem of simulating a cosmic microwave background (CMB) anisotropy sky map at the resolution typical of the high resolution maps produced by the Planck mission. For the main Planck CMB science channels we achieve a speedup of over a factor of ten, assuming an acceptable fractional rms error of order 10-5 in the power spectrum of the output map.

  13. Hardware efficient implementation of DFT using an improved first-order moments based cyclic convolution structure

    NASA Astrophysics Data System (ADS)

    Xiong, Jun; Liu, J. G.; Cao, Li

    2015-12-01

    This paper presents hardware efficient designs for implementing the one-dimensional (1D) discrete Fourier transform (DFT). Once DFT is formulated as the cyclic convolution form, the improved first-order moments-based cyclic convolution structure can be used as the basic computing unit for the DFT computation, which only contains a control module, a barrel shifter and (N-1)/2 accumulation units. After decomposing and reordering the twiddle factors, all that remains to do is shifting the input data sequence and accumulating them under the control of the statistical results on the twiddle factors. The whole calculation process only contains shift operations and additions with no need for multipliers and large memory. Compared with the previous first-order moments-based structure for DFT, the proposed designs have the advantages of less hardware consumption, lower power consumption and the flexibility to achieve better performance in certain cases. A series of experiments have proven the high performance of the proposed designs in terms of the area time product and power consumption. Similar efficient designs can be obtained for other computations, such as DCT/IDCT, DST/IDST, digital filter and correlation by transforming them into the forms of the first-order moments based cyclic convolution.

  14. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

    PubMed

    Pang, Shan; Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

  15. Free form fabrication of thermoplastic composites

    SciTech Connect

    Kaufman, S.G.; Spletzer, B.L.; Guess, T.R.

    1998-02-01

    This report describes the results of composites fabrication research sponsored by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories. They have developed, prototyped, and demonstrated the feasibility of a novel robotic technique for rapid fabrication of composite structures. Its chief innovation is that, unlike all other available fabrication methods, it does not require a mold. Instead, the structure is built patch by patch, using a rapidly reconfigurable forming surface, and a robot to position the evolving part. Both of these components are programmable, so only the control software needs to be changed to produce a new shape. Hence it should be possible to automatically program the system to produce a shape directly from an electronic model of it. It is therefore likely that the method will enable faster and less expensive fabrication of composites.

  16. Convolution-based estimation of organ dose in tube current modulated CT

    PubMed Central

    Tian, Xiaoyu; Segars, W Paul; Dixon, Robert L; Samei, Ehsan

    2016-01-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460–7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18–70 years, weight range: 60–180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients (hOrgan) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate (CTDIvol)organ, convolution values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying (CTDIvol)organ, convolution with the organ dose coefficients (hOrgan). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The discrepancy between the estimated organ dose and dose simulated using TCM Monte Carlo program was quantified. We further compared the

  17. Convolution-based estimation of organ dose in tube current modulated CT

    NASA Astrophysics Data System (ADS)

    Tian, Xiaoyu; Segars, W. Paul; Dixon, Robert L.; Samei, Ehsan

    2016-05-01

    Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460-7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18-70 years, weight range: 60-180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ({{h}\\text{Organ}} ) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} with the organ dose coefficients ({{h}\\text{Organ}} ). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The

  18. Effects of magnetic field on the shape memory behavior of single and polycrystalline magnetic shape memory alloys

    NASA Astrophysics Data System (ADS)

    Turabi, Ali Sadi

    Shape memory alloys and polymers have been extensively researched recently because of their unique ability to recover large deformations. Shape memory polymers (SMPs) are able to recover large deformations compared to shape memory alloys (SMAs), although SMAs have higher strength and are able to generate more stress during recovery. This project focuses on procedure for fabrication and Finite Element Modeling (FEM) of a shape memory composite actuator. First, SMP was characterized to reveal its mechanical properties. Specifically, glass transition temperature, the effects of temperature and strain rate on compressive response and recovery properties of shape memory polymer were studied. Then, shape memory properties of a NiTi wire, including transformation temperatures and stress generation, were investigated. SMC actuator was fabricated by using epoxy based SMP and NiTi SMA wire. Experimental tests confirmed the reversible behavior of fabricated shape memory composites. (Abstract shortened by ProQuest.).

  19. ITER Central Solenoid Module Fabrication

    SciTech Connect

    Smith, John

    2016-09-23

    The fabrication of the modules for the ITER Central Solenoid (CS) has started in a dedicated production facility located in Poway, California, USA. The necessary tools have been designed, built, installed, and tested in the facility to enable the start of production. The current schedule has first module fabrication completed in 2017, followed by testing and subsequent shipment to ITER. The Central Solenoid is a key component of the ITER tokamak providing the inductive voltage to initiate and sustain the plasma current and to position and shape the plasma. The design of the CS has been a collaborative effort between the US ITER Project Office (US ITER), the international ITER Organization (IO) and General Atomics (GA). GA’s responsibility includes: completing the fabrication design, developing and qualifying the fabrication processes and tools, and then completing the fabrication of the seven 110 tonne CS modules. The modules will be shipped separately to the ITER site, and then stacked and aligned in the Assembly Hall prior to insertion in the core of the ITER tokamak. A dedicated facility in Poway, California, USA has been established by GA to complete the fabrication of the seven modules. Infrastructure improvements included thick reinforced concrete floors, a diesel generator for backup power, along with, cranes for moving the tooling within the facility. The fabrication process for a single module requires approximately 22 months followed by five months of testing, which includes preliminary electrical testing followed by high current (48.5 kA) tests at 4.7K. The production of the seven modules is completed in a parallel fashion through ten process stations. The process stations have been designed and built with most stations having completed testing and qualification for carrying out the required fabrication processes. The final qualification step for each process station is achieved by the successful production of a prototype coil. Fabrication of the first

  20. Modeling of forced CVI for tube fabrication

    SciTech Connect

    Starr, T.L.; Smith, A.W.

    1994-05-01

    The forced flow/thermal gradient chemical vapor infiltration process (FCVI) can be used for fabrication of tube-shaped components of ceramic matrix composites. Recent experimental work at Oak Ridge National Laboratory (ORNL) includes process and materials development studies using a small tube reactor for tubes 20 cm long and 2.5 cm ID. Adaption of FCVI for this geometry involves significant changes in fixturing as compared to disk-shaped preforms previously fabricated. The authors have used this computer model of the CVI process to simulate tube densification and to identify process modifications that will decrease processing time.

  1. A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations.

    PubMed

    Roth, Holger R; Lu, Le; Seff, Ari; Cherry, Kevin M; Hoffman, Joanne; Wang, Shijun; Liu, Jiamin; Turkbey, Evrim; Summers, Ronald M

    2014-01-01

    Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards -100% sensitivity at the cost of high FP levels (-40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.

  2. A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations

    PubMed Central

    Lu, Le; Seff, Ari; Cherry, Kevin M.; Hoffman, Joanne; Wang, Shijun; Liu, Jiamin; Turkbey, Evrim; Summers, Ronald M.

    2015-01-01

    Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards ~100% sensitivity at the cost of high FP levels (~40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work. PMID:25333158

  3. Fabrication of recyclable superhydrophobic cotton fabrics

    NASA Astrophysics Data System (ADS)

    Han, Sang Wook; Park, Eun Ji; Jeong, Myung-Geun; Kim, Il Hee; Seo, Hyun Ook; Kim, Ju Hwan; Kim, Kwang-Dae; Kim, Young Dok

    2017-04-01

    Commercial cotton fabric was coated with SiO2 nanoparticles wrapped with a polydimethylsiloxane (PDMS) layer, and the resulting material surface showed a water contact angle greater than 160°. The superhydrophobic fabric showed resistance to water-soluble contaminants and maintained its original superhydrophobic properties with almost no alteration even after many times of absorption-washing cycles of oil. Moreover, superhydrophobic fabric can be used as a filter to separate oil from water. We demonstrated a simple method of fabrication of superhydrophobic fabric with potential interest for use in a variety of applications.

  4. Study of the Auger line shape of polyethylene and diamond

    NASA Technical Reports Server (NTRS)

    Dayan, M.; Pepper, S. V.

    1984-01-01

    The KVV Auger electron line shapes of carbon in polyethylene and diamond have been studied. The spectra were obtained in derivative form by electron beam excitation. They were treated by background subtraction, integration and deconvolution to produce the intrinsic Auger line shape. Electron energy loss spectra provided the response function in the deconvolution procedure. The line shape from polyethylene is compared with spectra from linear alkanes and with a previous spectrum of Kelber et al. Both spectra are compared with the self-convolution of their full valence band densities of states and of their p-projected densities. The experimental spectra could not be understood in terms of existing theories. This is so even when correlation effects are qualitatively taken into account account to the theories of Cini and Sawatzky and Lenselink.

  5. Fabricating customized hydrogel contact lens

    NASA Astrophysics Data System (ADS)

    Childs, Andre; Li, Hao; Lewittes, Daniella M.; Dong, Biqin; Liu, Wenzhong; Shu, Xiao; Sun, Cheng; Zhang, Hao F.

    2016-10-01

    Contact lenses are increasingly used in laboratories for in vivo animal retinal imaging and pre-clinical studies. The lens shapes often need modification to optimally fit corneas of individual test subjects. However, the choices from commercially available contact lenses are rather limited. Here, we report a flexible method to fabricate customized hydrogel contact lenses. We showed that the fabricated hydrogel is highly transparent, with refractive indices ranging from 1.42 to 1.45 in the spectra range from 400 nm to 800 nm. The Young’s modulus (1.47 MPa) and hydrophobicity (with a sessile drop contact angle of 40.5°) have also been characterized experimentally. Retinal imaging using optical coherence tomography in rats wearing our customized contact lenses has the quality comparable to the control case without the contact lens. Our method could significantly reduce the cost and the lead time for fabricating soft contact lenses with customized shapes, and benefit the laboratorial-used contact lenses in pre-clinical studies.

  6. Fabricating customized hydrogel contact lens

    PubMed Central

    Childs, Andre; Li, Hao; Lewittes, Daniella M.; Dong, Biqin; Liu, Wenzhong; Shu, Xiao; Sun, Cheng; Zhang, Hao F.

    2016-01-01

    Contact lenses are increasingly used in laboratories for in vivo animal retinal imaging and pre-clinical studies. The lens shapes often need modification to optimally fit corneas of individual test subjects. However, the choices from commercially available contact lenses are rather limited. Here, we report a flexible method to fabricate customized hydrogel contact lenses. We showed that the fabricated hydrogel is highly transparent, with refractive indices ranging from 1.42 to 1.45 in the spectra range from 400 nm to 800 nm. The Young’s modulus (1.47 MPa) and hydrophobicity (with a sessile drop contact angle of 40.5°) have also been characterized experimentally. Retinal imaging using optical coherence tomography in rats wearing our customized contact lenses has the quality comparable to the control case without the contact lens. Our method could significantly reduce the cost and the lead time for fabricating soft contact lenses with customized shapes, and benefit the laboratorial-used contact lenses in pre-clinical studies. PMID:27748361

  7. Fabricating customized hydrogel contact lens.

    PubMed

    Childs, Andre; Li, Hao; Lewittes, Daniella M; Dong, Biqin; Liu, Wenzhong; Shu, Xiao; Sun, Cheng; Zhang, Hao F

    2016-10-17

    Contact lenses are increasingly used in laboratories for in vivo animal retinal imaging and pre-clinical studies. The lens shapes often need modification to optimally fit corneas of individual test subjects. However, the choices from commercially available contact lenses are rather limited. Here, we report a flexible method to fabricate customized hydrogel contact lenses. We showed that the fabricated hydrogel is highly transparent, with refractive indices ranging from 1.42 to 1.45 in the spectra range from 400 nm to 800 nm. The Young's modulus (1.47 MPa) and hydrophobicity (with a sessile drop contact angle of 40.5°) have also been characterized experimentally. Retinal imaging using optical coherence tomography in rats wearing our customized contact lenses has the quality comparable to the control case without the contact lens. Our method could significantly reduce the cost and the lead time for fabricating soft contact lenses with customized shapes, and benefit the laboratorial-used contact lenses in pre-clinical studies.

  8. BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

    PubMed

    Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

    2017-02-01

    We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain.

  9. Patient-specific dosimetry using quantitative SPECT imaging and three-dimensional discrete fourier transform convolution

    SciTech Connect

    Akabani, G.; Hawkins, W.G.; Eckblade, M.B.; Leichner, P.K.

    1997-02-01

    The objective of this study was to develop a three-dimensional discrete Fourier transform (3D-DFT) convolution method to perform the dosimetry for {sup 131}I-labeled antibodies in soft tissues. Mathematical and physical phantoms were used to compare 3D-DFT with Monte Carlo transport (MCT) calculations based on the EGS4 code. The mathematical and physical phantoms consisted of a sphere and cylinder, respectively, containing uniform and nonuniform activity distributions. Quantitative SPECT reconstruction was carried out using the circular harmonic transform (CHT) algorithm. The radial dose profile obtained from MCT calculations and the 3D-DFT convolution method for the mathematical phantom were in close agreement. The root mean square error (RMSE) for the two methods was <0.1%, with a maximum difference <21%. Results obtained for the physical phantom gave a RMSE <0.1% and a maximum difference of <13%; isodose contours were in good agreement. SPECT data for two patients who had undergone {sup 131}I radioimmunotherapy (RIT) were used to compare absorbed-dose rates and isodose rate contours with the two methods of calculations. This yielded a RMSE <0.02% and a maximum difference of <13%. Our results showed that the 3D-DFT convolution method compared well with MCT calculations. The 3D-DFT approach is computationally much more efficient and, hence, the method of choice. This method is patient-specific and applicable to the dosimetry of soft-tissue tumors and normal organs. It can be implemented on personal computers. 22 refs., 6 figs., 2 tabs.

  10. On the mechanism of parathyroid hormone stimulation of calcium uptake by mouse distal convoluted tubule cells.

    PubMed Central

    Gesek, F A; Friedman, P A

    1992-01-01

    PTH stimulates transcellular Ca2+ absorption in renal distal convoluted tubules. The effect of PTH on membrane voltage, the ionic basis of the change in voltage, and the relations between voltage and calcium entry were determined on immortalized mouse distal convoluted tubule cells. PTH (10(-8) M) significantly increased 45Ca2+ uptake from basal levels of 2.81 +/- 0.16 to 3.88 +/- 0.19 nmol min-1 mg protein-1. PTH-induced 45Ca2+ uptake was abolished by the dihydropyridine antagonist, nifedipine (10(-5) M). PTH did not affect 22Na+ uptake. Intracellular calcium activity ([Ca2+]i) was measured in cells loaded with fura-2. Control [Ca2+]i averaged 112 +/- 21 nM. PTH increased [Ca2+]i over the range of 10(-11) to 10(-7) M. Maximal stimulation to 326 +/- 31 nM was achieved at 10(-8) M PTH. Resting membrane voltage measured with the potential sensitive dye DiO6(3) averaged -71 +/- 2 mV. PTH hyperpolarized cells by 19 +/- 4 mV. The chloride-channel blocker NPPB prevented PTH-induced hyperpolarization. PTH decreased and NPPB increased intracellular chloride, measured with the fluorescent dye SPQ. Chloride permeability was estimated by measuring the rate of 125I- efflux. PTH increased 125I- efflux and this effect was blocked by NPPB. Clamping voltage with K+/valinomycin; depolarizing membrane voltage by reducing extracellular chloride; or addition of NPPB prevented PTH-induced calcium uptake. In conclusion, PTH increases chloride conductance in distal convoluted tubule cells leading to decreased intracellular chloride activity, membrane hyperpolarization, and increased calcium entry through dihydropyridine-sensitive calcium channels. PMID:1522230

  11. Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Volpi, Michele; Tuia, Devis

    2017-02-01

    Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial data, with large appearance variations. Convolutional Neural Networks (CNNs) achieve this goal by learning discriminatively a hierarchy of representations of increasing abstraction. In this paper we present a CNN-based system relying on an downsample-then-upsample architecture. Specifically, it first learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them back to the original resolution by deconvolutions. By doing so, the CNN learns to densely label every pixel at the original resolution of the image. This results in many advantages, including i) state-of-the-art numerical accuracy, ii) improved geometric accuracy of predictions and iii) high efficiency at inference time. We test the proposed system on the Vaihingen and Potsdam sub-decimeter resolution datasets, involving semantic labeling of aerial images of 9cm and 5cm resolution, respectively. These datasets are composed by many large and fully annotated tiles allowing an unbiased evaluation of models making use of spatial information. We do so by comparing two standard CNN architectures to the proposed one: standard patch classification, prediction of local label patches by employing only convolutions and full patch labeling by employing deconvolutions. All the systems compare favorably or outperform a state-of-the-art baseline relying on superpixels and powerful appearance descriptors. The proposed full patch labeling CNN outperforms these models by a large margin, also showing a very appealing inference time.

  12. Collapsed cone convolution of radiant energy for photon dose calculation in heterogeneous media.

    PubMed

    Ahnesjö, A

    1989-01-01

    A method for photon beam dose calculations is described. The primary photon beam is raytraced through the patient, and the distribution of total radiant energy released into the patient is calculated. Polyenergetic energy deposition kernels are calculated from the spectrum of the beam, using a database of monoenergetic kernels. It is shown that the polyenergetic kernels can be analytically described with high precision by (A exp( -ar) + B exp( -br)/r2, where A, a, B, and b depend on the angle with respect to the impinging photons and the accelerating potential, and r is the radial distance. Numerical values of A, a, B, and b are derived and used to convolve energy deposition kernels with the total energy released per unit mass (TERMA) to yield dose distributions. The convolution is facilitated by the introduction of the collapsed cone approximation. In this approximation, all energy released into coaxial cones of equal solid angle, from volume elements on the cone axis, is rectilinearly transported, attenuated, and deposited in elements on the axis. Scaling of the kernels is implicitly done during the convolution procedure to fully account for inhomogeneities present in the irradiated volume. The number of computational operations needed to compute the dose with the method is proportional to the number of calculation points. The method is tested for five accelerating potentials; 4, 6, 10, 15, and 24 MV, and applied to two geometries; one is a stack of slabs of tissue media, and the other is a mediastinum-like phantom of cork and water. In these geometries, the EGS4 Monte Carlo system has been used to generate reference dose distributions with which the dose computed with the collapsed cone convolution method is compared. Generally, the agreement between the methods is excellent. Deviations are observed in situations of lateral charged particle disequilibrium in low density media, however, but the result is superior compared to that of the generalized Batho method.

  13. De-convoluting mixed crude oil in Prudhoe Bay Field, North Slope, Alaska

    USGS Publications Warehouse

    Peters, K.E.; Scott, Ramos L.; Zumberge, J.E.; Valin, Z.C.; Bird, K.J.

    2008-01-01

    Seventy-four crude oil samples from the Barrow arch on the North Slope of Alaska were studied to assess the relative volumetric contributions from different source rocks to the giant Prudhoe Bay Field. We applied alternating least squares to concentration data (ALS-C) for 46 biomarkers in the range C19-C35 to de-convolute mixtures of oil generated from carbonate rich Triassic Shublik Formation and clay rich Jurassic Kingak Shale and Cretaceous Hue Shale-gamma ray zone (Hue-GRZ) source rocks. ALS-C results for 23 oil samples from the prolific Ivishak Formation reservoir of the Prudhoe Bay Field indicate approximately equal contributions from Shublik Formation and Hue-GRZ source rocks (37% each), less from the Kingak Shale (26%), and little or no contribution from other source rocks. These results differ from published interpretations that most oil in the Prudhoe Bay Field originated from the Shublik Formation source rock. With few exceptions, the relative contribution of oil from the Shublik Formation decreases, while that from the Hue-GRZ increases in reservoirs along the Barrow arch from Point Barrow in the northwest to Point Thomson in the southeast (???250 miles or 400 km). The Shublik contribution also decreases to a lesser degree between fault blocks within the Ivishak pool from west to east across the Prudhoe Bay Field. ALS-C provides a robust means to calculate the relative amounts of two or more oil types in a mixture. Furthermore, ALS-C does not require that pure end member oils be identified prior to analysis or that laboratory mixtures of these oils be prepared to evaluate mixing. ALS-C of biomarkers reliably de-convolutes mixtures because the concentrations of compounds in mixtures vary as linear functions of the amount of each oil type. ALS of biomarker ratios (ALS-R) cannot be used to de-convolute mixtures because compound ratios vary as nonlinear functions of the amount of each oil type.

  14. Dip-molded t-shaped cannula

    NASA Technical Reports Server (NTRS)

    Broyles, H. F.; Cuddihy, E. F.; Moacanin, J.

    1978-01-01

    Cannula, fabricated out of polyetherurethane, has been designed for long-term service. Improved cannula is T-shaped to collect blood from both directions, thus replacing two conventional cannulas that are usually required and eliminating need for large surgical wound. It is fabricated by using dip-molding process that can be adapted to other elastomeric objects having complex shapes. Dimensions of cannula were chosen to optimize its blood-flow properties and to reduce danger of excessive clotting, making it suitable for continuous service up to 21 days in vein or artery of patient.

  15. convolve_image.pro: Common-Resolution Convolution Kernels for Space- and Ground-Based Telescopes

    NASA Astrophysics Data System (ADS)

    Aniano, Gonzalo J.

    2014-01-01

    The IDL package convolve_image.pro transforms images between different instrumental point spread functions (PSFs). It can load an image file and corresponding kernel and return the convolved image, thus preserving the colors of the astronomical sources. Convolution kernels are available for images from Spitzer (IRAC MIPS), Herschel (PACS SPIRE), GALEX (FUV NUV), WISE (W1 - W4), Optical PSFs (multi- Gaussian and Moffat functions), and Gaussian PSFs; they allow the study of the Spectral Energy Distribution (SED) of extended objects and preserve the characteristic SED in each pixel.

  16. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision.

    PubMed

    Zhong, Bineng; Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method.

  17. Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

    PubMed Central

    Pan, Shengnan; Zhang, Hongbo; Wang, Tian; Du, Jixiang; Chen, Duansheng; Cao, Liujuan

    2016-01-01

    In this paper, we propose deep architecture to dynamically learn the most discriminative features from data for both single-cell and object tracking in computational biology and computer vision. Firstly, the discriminative features are automatically learned via a convolutional deep belief network (CDBN). Secondly, we design a simple yet effective method to transfer features learned from CDBNs on the source tasks for generic purpose to the object tracking tasks using only limited amount of training data. Finally, to alleviate the tracker drifting problem caused by model updating, we jointly consider three different types of positive samples. Extensive experiments validate the robustness and effectiveness of the proposed method. PMID:27847827

  18. Using convolutional neural networks for human activity classification on micro-Doppler radar spectrograms

    NASA Astrophysics Data System (ADS)

    Jordan, Tyler S.

    2016-05-01

    This paper presents the findings of using convolutional neural networks (CNNs) to classify human activity from micro-Doppler features. An emphasis on activities involving potential security threats such as holding a gun are explored. An automotive 24 GHz radar on chip was used to collect the data and a CNN (normally applied to image classification) was trained on the resulting spectrograms. The CNN achieves an error rate of 1.65 % on classifying running vs. walking, 17.3 % error on armed walking vs. unarmed walking, and 22 % on classifying six different actions.

  19. Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding

    PubMed Central

    Johnson, Rie; Zhang, Tong

    2016-01-01

    This paper presents a new semi-supervised framework with convolutional neural networks (CNNs) for text categorization. Unlike the previous approaches that rely on word embeddings, our method learns embeddings of small text regions from unlabeled data for integration into a supervised CNN. The proposed scheme for embedding learning is based on the idea of two-view semi-supervised learning, which is intended to be useful for the task of interest even though the training is done on unlabeled data. Our models achieve better results than previous approaches on sentiment classification and topic classification tasks. PMID:27087766

  20. tf_unet: Generic convolutional neural network U-Net implementation in Tensorflow

    NASA Astrophysics Data System (ADS)

    Akeret, Joel; Chang, Chihway; Lucchi, Aurelien; Refregier, Alexandre

    2016-11-01

    tf_unet mitigates radio frequency interference (RFI) signals in radio data using a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. The code is not tied to a specific segmentation and can be used, for example, to detect radio frequency interference (RFI) in radio astronomy or galaxies and stars in widefield imaging data. This U-Net implementation can outperform classical RFI mitigation algorithms.

  1. Multi-Scale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification.

    PubMed

    Wang, Qiangchang; Zheng, Yuanjie; Yang, Gongping; Jin, Weidong; Chen, Xinjian; Yin, Yilong

    2017-03-21

    We propose a new Multi-scale Rotation-invariant Convolutional Neural Network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography (HRCT). MRCNN employs Gabor-local binary pattern (Gabor-LBP) which introduces a good property in image analysis - invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public Interstitial Lung Disease (ILD) database show a superior performance of the proposed method to state-of-the-art.

  2. Satellite image processing for precision agriculture and agroindustry using convolutional neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Firdaus; Arkeman, Y.; Buono, A.; Hermadi, I.

    2017-01-01

    Translating satellite imagery to a useful data for decision making during this time are usually done manually by human. In this research, we are going to translate satellite imagery by using artificial intelligence method specifically using convolutional neural network and genetic algorithm to become a useful data for decision making, especially for precision agriculture and agroindustry. In this research, we are focused on how to made a sustainable land use planning with 3 objectives. The first is maximizing economic factor. Second is minimizing CO2 emission and the last is minimizing land degradation. Results show that by using artificial intelligence method, can produced a good pareto optimum solutions in a short time.

  3. Parity retransmission hybrid ARQ using rate 1/2 convolutional codes on a nonstationary channel

    NASA Technical Reports Server (NTRS)

    Lugand, Laurent R.; Costello, Daniel J., Jr.; Deng, Robert H.

    1989-01-01

    A parity retransmission hybrid automatic repeat request (ARQ) scheme is proposed which uses rate 1/2 convolutional codes and Viterbi decoding. A protocol is described which is capable of achieving higher throughputs than previously proposed parity retransmission schemes. The performance analysis is based on a two-state Markov model of a nonstationary channel. This model constitutes a first approximation to a nonstationary channel. The two-state channel model is used to analyze the throughput and undetected error probability of the protocol presented when the receiver has both an infinite and a finite buffer size. It is shown that the throughput improves as the channel becomes more bursty.

  4. Diffuse dispersive delay and the time convolution/attenuation of transients

    NASA Technical Reports Server (NTRS)

    Bittner, Burt J.

    1991-01-01

    Test data and analytic evaluations are presented to show that relatively poor 100 KHz shielding of 12 Db can effectively provide an electromagnetic pulse transient reduction of 100 Db. More importantly, several techniques are shown for lightning surge attenuation as an alternative to crowbar, spark gap, or power zener type clipping which simply reflects the surge. A time delay test method is shown which allows CW testing, along with a convolution program to define transient shielding effectivity where the Fourier phase characteristics of the transient are known or can be broadly estimated.

  5. Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features

    NASA Astrophysics Data System (ADS)

    Obeso, Abraham Montoya; Benois-Pineau, Jenny; Acosta, Alejandro Álvaro Ramirez; Vázquez, Mireya Saraí García

    2017-01-01

    We propose a convolutional neural network to classify images of buildings using sparse features at the network's input in conjunction with primary color pixel values. As a result, a trained neuronal model is obtained to classify Mexican buildings in three classes according to the architectural styles: prehispanic, colonial, and modern with an accuracy of 88.01%. The problem of poor information in a training dataset is faced due to the unequal availability of cultural material. We propose a data augmentation and oversampling method to solve this problem. The results are encouraging and allow for prefiltering of the content in the search tasks.

  6. Processing circuit with asymmetry corrector and convolutional encoder for digital data

    NASA Technical Reports Server (NTRS)

    Pfiffner, Harold J. (Inventor)

    1987-01-01

    A processing circuit is provided for correcting for input parameter variations, such as data and clock signal symmetry, phase offset and jitter, noise and signal amplitude, in incoming data signals. An asymmetry corrector circuit performs the correcting function and furnishes the corrected data signals to a convolutional encoder circuit. The corrector circuit further forms a regenerated clock signal from clock pulses in the incoming data signals and another clock signal at a multiple of the incoming clock signal. These clock signals are furnished to the encoder circuit so that encoded data may be furnished to a modulator at a high data rate for transmission.

  7. Models For Diffracting Aperture Identification : A Comparison Between Ideal And Convolutional Observations

    NASA Astrophysics Data System (ADS)

    Crosta, Giovanni

    1983-09-01

    We consider a number of inverse diffraction problems where different models are compared. Ideal measurements yield Cauchy data , to which corresponds a unique solution. If a convolutional observation map is chosen, uniqueness can no longer be insured. We also briefly examine a non-linear non-invertible observation map , which describes a quadratic detector. In all of these cases we discuss the link between aperture identification and optimal control theory , which leads to regularised functional minimisation. This task can be performed by a discrete gradient algorithm of which we give the flow chart.

  8. Polymorphous computing fabric

    DOEpatents

    Wolinski, Christophe Czeslaw [Los Alamos, NM; Gokhale, Maya B [Los Alamos, NM; McCabe, Kevin Peter [Los Alamos, NM

    2011-01-18

    Fabric-based computing systems and methods are disclosed. A fabric-based computing system can include a polymorphous computing fabric that can be customized on a per application basis and a host processor in communication with said polymorphous computing fabric. The polymorphous computing fabric includes a cellular architecture that can be highly parameterized to enable a customized synthesis of fabric instances for a variety of enhanced application performances thereof. A global memory concept can also be included that provides the host processor random access to all variables and instructions associated with the polymorphous computing fabric.

  9. Shape memory composite antennas for space applications

    NASA Astrophysics Data System (ADS)

    Santo, Loredana; Quadrini, Fabrizio; Bellisario, Denise

    2016-11-01

    Future space missions will require large space infrastructures in order to achieve scientific and technological objectives characterized by an intrinsic complexity. In this study, the development of shape memory composite structures for aerospace applications is described. In particular, the structure of a small-scale self-deployable mast has been prototyped as a proof of concept for its feasibility. The mast structure is made by interlocking two shape memory polymer composite (SMPC) strips, each one made of two layers of carbon fiber fabric with a shape memory (SM) epoxy resin interlayer. A complete deployment of the SMC structure was achieved. The versatility of this technology has been also demonstrated in previous studies, in which small scale deploying solar panels were fabricated. Obtained results are very promising in terms of manufacturing technology, and shape recovery of manufactured parts.

  10. Chemically enabled nanostructure fabrication

    NASA Astrophysics Data System (ADS)

    Huo, Fengwei

    The first part of the dissertation explored ways of chemically synthesizing new nanoparticles and biologically guided assembly of nanoparticle building blocks. Chapter two focuses on synthesizing three-layer composite magnetic nanoparticles with a gold shell which can be easily functionalized with other biomolecules. The three-layer magnetic nanoparticles, when functionalized with oligonucleotides, exhibit the surface chemistry, optical properties, and cooperative DNA binding properties of gold nanoparticle probes, while maintaining the magnetic properties of the Fe3O4 inner shell. Chapter three describes a new method for synthesizing nanoparticles asymmetrically functionalized with oligonucleotides and the use of these novel building blocks to create satellite structures. This synthetic capability allows one to introduce valency into such structures and then use that valency to direct particle assembly events. The second part of the thesis explored approaches of nanostructure fabrication on substrates. Chapter four focuses on the development of a new scanning probe contact printing method, polymer pen lithography (PPL), which combines the advantages of muCp and DPN to achieve high-throughput, flexible molecular printing. PPL uses a soft elastomeric tip array, rather than tips mounted on individual cantilevers, to deliver inks to a surface in a "direct write" manner. Arrays with as many as ˜11 million pyramid-shaped pens can be brought into contact with substrates and readily leveled optically in order to insure uniform pattern development. Chapter five describes gel pen lithography, which uses a gel to fabricate pen array. Gel pen lithography is a low-cost, high-throughput nanolithography method especially useful for biomaterials patterning and aqueous solution patterning which makes it a supplement to DPN and PPL. Chapter 6 shows a novel form of optical nanolithography, Beam Pen Lithography (BPL), which uses an array of NSOM pens to do nanoscale optical

  11. Material cutting, shaping, and forming: A compilation

    NASA Technical Reports Server (NTRS)

    1974-01-01

    Information is presented concerning cutting, shaping, and forming of materials, and the equipment and techniques required for utilizing these materials. The use of molds, electrical fields, and mechanical devices are related to forming materials. Material cutting methods by devices including borers and slicers are presented along with chemical techniques. Shaping and fabrication techniques are described for tubing, honeycomb panels, and ceramic structures. The characteristics of the materials are described. Patent information is included.

  12. Fabrication of zein nanostructure

    NASA Astrophysics Data System (ADS)

    Luecha, Jarupat

    resins. The soft lithography technique was mainly used to fabricate micro and nanostructures on zein films. Zein material well-replicated small structures with the smallest size at sub micrometer scale that resulted in interesting photonic properties. The bonding method was also developed for assembling portable zein microfluidic devices with small shape distortion. Zein-zein and zein-glass microfluidic devices demonstrated sufficient strength to facilitate fluid flow in a complex microfluidic design with no leakage. Aside from the fabrication technique development, several potential applications of this environmentally friendly microfluidic device were investigated. The concentration gradient manipulation of Rhodamine B solution in zein-glass microfluidic devices was demonstrated. The diffusion of small molecules such as fluorescent dye into the wall of the zein microfluidic channels was observed. However, with this formulation, zein microfluidic devices were not suitable for cell culture applications. This pioneer study covered a wide spectrum of the implementation of the two nanotechnology approaches to advance zein biomaterial which provided proof of fundamental concepts as well as presenting some limitations. The findings in this study can lead to several innovative research opportunities of advanced zein biomaterials with broad applications. The information from the study of zein nanocomposite structure allows the packaging industry to develop the low cost biodegradable materials with physical property improvement. The information from the study of the zein microfluidic devices allows agro-industry to develop the nanotechnology-enabled microfluidic sensors fabricated entirely from biodegradable polymer for on-site disease or contaminant detection in the fields of food and agriculture.

  13. Superordinate Shape Classification Using Natural Shape Statistics

    ERIC Educational Resources Information Center

    Wilder, John; Feldman, Jacob; Singh, Manish

    2011-01-01

    This paper investigates the classification of shapes into broad natural categories such as "animal" or "leaf". We asked whether such coarse classifications can be achieved by a simple statistical classification of the shape skeleton. We surveyed databases of natural shapes, extracting shape skeletons and tabulating their…

  14. Shape Determination for Deformed Electromagnetic Cavities

    SciTech Connect

    Akcelik, Volkan; Ko, Kwok; Lee, Lie-Quan; Li, Zhenghai; Ng, Cho-Kuen; Xiao, Liling; /SLAC

    2007-12-10

    The measured physical parameters of a superconducting cavity differ from those of the designed ideal cavity. This is due to shape deviations caused by both loose machine tolerances during fabrication and by the tuning process for the accelerating mode. We present a shape determination algorithm to solve for the unknown deviations from the ideal cavity using experimentally measured cavity data. The objective is to match the results of the deformed cavity model to experimental data through least-squares minimization. The inversion variables are unknown shape deformation parameters that describe perturbations of the ideal cavity. The constraint is the Maxwell eigenvalue problem. We solve the nonlinear optimization problem using a line-search based reduced space Gauss-Newton method where we compute shape sensitivities with a discrete adjoint approach. We present two shape determination examples, one from synthetic and the other from experimental data. The results demonstrate that the proposed algorithm is very effective in determining the deformed cavity shape.

  15. Concatenated coding systems employing a unit-memory convolutional code and a byte-oriented decoding algorithm

    NASA Technical Reports Server (NTRS)

    Lee, L. N.

    1976-01-01

    Concatenated coding systems utilizing a convolutional code as the inner code and a Reed-Solomon code as the outer code are considered. In order to obtain very reliable communications over a very noisy channel with relatively small coding complexity, it is proposed to concatenate a byte oriented unit memory convolutional code with an RS outer code whose symbol size is one byte. It is further proposed to utilize a real time minimal byte error probability decoding algorithm, together with feedback from the outer decoder, in the decoder for the inner convolutional code. The performance of the proposed concatenated coding system is studied, and the improvement over conventional concatenated systems due to each additional feature is isolated.

  16. Revision of the theory of tracer transport and the convolution model of dynamic contrast enhanced magnetic resonance imaging

    PubMed Central

    Bammer, Roland; Stollberger, Rudolf

    2012-01-01

    Counterexamples are used to motivate the revision of the established theory of tracer transport. Then dynamic contrast enhanced magnetic resonance imaging in particular is conceptualized in terms of a fully distributed convection–diffusion model from which a widely used convolution model is derived using, alternatively, compartmental discretizations or semigroup theory. On this basis, applications and limitations of the convolution model are identified. For instance, it is proved that perfusion and tissue exchange states cannot be identified on the basis of a single convolution equation alone. Yet under certain assumptions, particularly that flux is purely convective at the boundary of a tissue region, physiological parameters such as mean transit time, effective volume fraction, and volumetric flow rate per unit tissue volume can be deduced from the kernel. PMID:17429633

  17. Simplified Fabrication of Helical Copper Antennas

    NASA Technical Reports Server (NTRS)

    Petro, Andrew

    2006-01-01

    A simplified technique has been devised for fabricating helical antennas for use in experiments on radio-frequency generation and acceleration of plasmas. These antennas are typically made of copper (for electrical conductivity) and must have a specific helical shape and precise diameter.

  18. Large patch convolutional neural networks for the scene classification of high spatial resolution imagery

    NASA Astrophysics Data System (ADS)

    Zhong, Yanfei; Fei, Feng; Zhang, Liangpei

    2016-04-01

    The increase of the spatial resolution of remote-sensing sensors helps to capture the abundant details related to the semantics of surface objects. However, it is difficult for the popular object-oriented classification approaches to acquire higher level semantics from the high spatial resolution remote-sensing (HSR-RS) images, which is often referred to as the "semantic gap." Instead of designing sophisticated operators, convolutional neural networks (CNNs), a typical deep learning method, can automatically discover intrinsic feature descriptors from a large number of input images to bridge the semantic gap. Due to the small data volume of the available HSR-RS scene datasets, which is far away from that of the natural scene datasets, there have been few reports of CNN approaches for HSR-RS image scene classifications. We propose a practical CNN architecture for HSR-RS scene classification, named the large patch convolutional neural network (LPCNN). The large patch sampling is used to generate hundreds of possible scene patches for the feature learning, and a global average pooling layer is used to replace the fully connected network as the classifier, which can greatly reduce the total parameters. The experiments confirm that the proposed LPCNN can learn effective local features to form an effective representation for different land-use scenes, and can achieve a performance that is comparable to the state-of-the-art on public HSR-RS scene datasets.

  19. Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

    NASA Astrophysics Data System (ADS)

    Liu, Ziyi; Gao, Junfeng; Yang, Guoguo; Zhang, Huan; He, Yong

    2016-02-01

    We present a pipeline for the visual localization and classification of agricultural pest insects by computing a saliency map and applying deep convolutional neural network (DCNN) learning. First, we used a global contrast region-based approach to compute a saliency map for localizing pest insect objects. Bounding squares containing targets were then extracted, resized to a fixed size, and used to construct a large standard database called Pest ID. This database was then utilized for self-learning of local image features which were, in turn, used for classification by DCNN. DCNN learning optimized the critical parameters, including size, number and convolutional stride of local receptive fields, dropout ratio and the final loss function. To demonstrate the practical utility of using DCNN, we explored different architectures by shrinking depth and width, and found effective sizes that can act as alternatives for practical applications. On the test set of paddy field images, our architectures achieved a mean Accuracy Precision (mAP) of 0.951, a significant improvement over previous methods.

  20. Emotional textile image classification based on cross-domain convolutional sparse autoencoders with feature selection

    NASA Astrophysics Data System (ADS)

    Li, Zuhe; Fan, Yangyu; Liu, Weihua; Yu, Zeqi; Wang, Fengqin

    2017-01-01

    We aim to apply sparse autoencoder-based unsupervised feature learning to emotional semantic analysis for textile images. To tackle the problem of limited training data, we present a cross-domain feature learning scheme for emotional textile image classification using convolutional autoencoders. We further propose a correlation-analysis-based feature selection method for the weights learned by sparse autoencoders to reduce the number of features extracted from large size images. First, we randomly collect image patches on an unlabeled image dataset in the source domain and learn local features with a sparse autoencoder. We then conduct feature selection according to the correlation between different weight vectors corresponding to the autoencoder's hidden units. We finally adopt a convolutional neural network including a pooling layer to obtain global feature activations of textile images in the target domain and send these global feature vectors into logistic regression models for emotional image classification. The cross-domain unsupervised feature learning method achieves 65% to 78% average accuracy in the cross-validation experiments corresponding to eight emotional categories and performs better than conventional methods. Feature selection can reduce the computational cost of global feature extraction by about 50% while improving classification performance.

  1. Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

    PubMed Central

    Ordóñez, Francisco Javier; Roggen, Daniel

    2016-01-01

    Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

  2. Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis.

    PubMed

    Hoogi, Assaf; Subramaniam, Arjun; Veerapaneni, Rishi; Rubin, Daniel

    2016-11-11

    In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNNbased and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of ����.�������� with our method (p < 0.001, Wilcoxon).

  3. Scatter-to-primary based scatter fractions for transmission-dependent convolution subtraction of SPECT images.

    PubMed

    Larsson, Anne; Johansson, Lennart

    2003-11-21

    In single photon emission computed tomography (SPECT), transmission-dependent convolution subtraction has been shown to be useful when correcting for scattered events. The method is based on convolution subtraction, but includes a matrix of scatter fractions instead of a global scatter fraction. The method can be extended to iteratively improve the scatter estimate, but in this note we show that this requires a modification of the theory to use scatter-to-total scatter fractions for the first iteration only and scatter-to-primary fractions thereafter. To demonstrate this, scatter correction is performed on a Monte Carlo simulated image of a point source of activity in water. The modification of the theory is compared to corrections where the scatter fractions are based on the scatter-to-total ratio, using one and ten iterations. The resulting ratios of subtracted to original counts are compared to the true scatter-to-total ratio of the simulation and the most accurate result is found for our modification of the theory.

  4. Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier

    NASA Astrophysics Data System (ADS)

    Rocco, Dominick Rosario

    The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of $[special characters omitted]. and theta23 and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74 x 1020 POT, 14 kt equivalent) to provide new best-fit measurements of sin2(theta23) = 0.43 (with a statistically-degenerate compliment near 0.60) and [special characters omitted]..

  5. Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis.

    PubMed

    Hoogi, Assaf; Subramaniam, Arjun; Veerapaneni, Rishi; Rubin, Daniel

    2016-11-11

    In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112 CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNNbased and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of 0.24 with our method (p < 0.001, Wilcoxon).

  6. The probabilistic convolution tree: efficient exact Bayesian inference for faster LC-MS/MS protein inference.

    PubMed

    Serang, Oliver

    2014-01-01

    Exact Bayesian inference can sometimes be performed efficiently for special cases where a function has commutative and associative symmetry of its inputs (called "causal independence"). For this reason, it is desirable to exploit such symmetry on big data sets. Here we present a method to exploit a general form of this symmetry on probabilistic adder nodes by transforming those probabilistic adder nodes into a probabilistic convolution tree with which dynamic programming computes exact probabilities. A substantial speedup is demonstrated using an illustration example that can arise when identifying splice forms with bottom-up mass spectrometry-based proteomics. On this example, even state-of-the-art exact inference algorithms require a runtime more than exponential in the number of splice forms considered. By using the probabilistic convolution tree, we reduce the runtime to O(k log(k)2) and the space to O(k log(k)) where k is the number of variables joined by an additive or cardinal operator. This approach, which can also be used with junction tree inference, is applicable to graphs with arbitrary dependency on counting variables or cardinalities and can be used on diverse problems and fields like forward error correcting codes, elemental decomposition, and spectral demixing. The approach also trivially generalizes to multiple dimensions.

  7. Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

    NASA Astrophysics Data System (ADS)

    Salamon, Justin; Bello, Juan Pablo

    2017-03-01

    The ability of deep convolutional neural networks (CNN) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models. This study has two primary contributions: first, we propose a deep convolutional neural network architecture for environmental sound classification. Second, we propose the use of audio data augmentation for overcoming the problem of data scarcity and explore the influence of different augmentations on the performance of the proposed CNN architecture. Combined with data augmentation, the proposed model produces state-of-the-art results for environmental sound classification. We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a "shallow" dictionary learning model with augmentation. Finally, we examine the influence of each augmentation on the model's classification accuracy for each class, and observe that the accuracy for each class is influenced differently by each augmentation, suggesting that the performance of the model could be improved further by applying class-conditional data augmentation.

  8. Performance of convolutional codes on fading channels typical of planetary entry missions

    NASA Technical Reports Server (NTRS)

    Modestino, J. W.; Mui, S. Y.; Reale, T. J.

    1974-01-01

    The performance of convolutional codes in fading channels typical of the planetary entry channel is examined in detail. The signal fading is due primarily to turbulent atmospheric scattering of the RF signal transmitted from an entry probe through a planetary atmosphere. Short constraint length convolutional codes are considered in conjunction with binary phase-shift keyed modulation and Viterbi maximum likelihood decoding, and for longer constraint length codes sequential decoding utilizing both the Fano and Zigangirov-Jelinek (ZJ) algorithms are considered. Careful consideration is given to the modeling of the channel in terms of a few meaningful parameters which can be correlated closely with theoretical propagation studies. For short constraint length codes the bit error probability performance was investigated as a function of E sub b/N sub o parameterized by the fading channel parameters. For longer constraint length codes the effect was examined of the fading channel parameters on the computational requirements of both the Fano and ZJ algorithms. The effects of simple block interleaving in combatting the memory of the channel is explored, using the analytic approach or digital computer simulation.

  9. Drug drug interaction extraction from biomedical literature using syntax convolutional neural network

    PubMed Central

    Zhao, Zhehuan; Yang, Zhihao; Luo, Ling; Lin, Hongfei; Wang, Jian

    2016-01-01

    Motivation: Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve. Results: In this article, we present a syntax convolutional neural network (SCNN) based DDI extraction method. In this method, a novel word embedding, syntax word embedding, is proposed to employ the syntactic information of a sentence. Then the position and part of speech features are introduced to extend the embedding of each word. Later, auto-encoder is introduced to encode the traditional bag-of-words feature (sparse 0–1 vector) as the dense real value vector. Finally, a combination of embedding-based convolutional features and traditional features are fed to the softmax classifier to extract DDIs from biomedical literature. Experimental results on the DDIExtraction 2013 corpus show that SCNN obtains a better performance (an F-score of 0.686) than other state-of-the-art methods. Availability and Implementation: The source code is available for academic use at http://202.118.75.18:8080/DDI/SCNN-DDI.zip. Contact: yangzh@dlut.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27466626

  10. Efficient pedestrian detection from aerial vehicles with object proposals and deep convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Minnehan, Breton; Savakis, Andreas

    2016-05-01

    As Unmanned Aerial Systems grow in numbers, pedestrian detection from aerial platforms is becoming a topic of increasing importance. By providing greater contextual information and a reduced potential for occlusion, the aerial vantage point provided by Unmanned Aerial Systems is highly advantageous for many surveillance applications, such as target detection, tracking, and action recognition. However, due to the greater distance between the camera and scene, targets of interest in aerial imagery are generally smaller and have less detail. Deep Convolutional Neural Networks (CNN's) have demonstrated excellent object classification performance and in this paper we adopt them to the problem of pedestrian detection from aerial platforms. We train a CNN with five layers consisting of three convolution-pooling layers and two fully connected layers. We also address the computational inefficiencies of the sliding window method for object detection. In the sliding window configuration, a very large number of candidate patches are generated from each frame, while only a small number of them contain pedestrians. We utilize the Edge Box object proposal generation method to screen candidate patches based on an "objectness" criterion, so that only regions that are likely to contain objects are processed. This method significantly reduces the number of image patches processed by the neural network and makes our classification method very efficient. The resulting two-stage system is a good candidate for real-time implementation onboard modern aerial vehicles. Furthermore, testing on three datasets confirmed that our system offers high detection accuracy for terrestrial pedestrian detection in aerial imagery.

  11. Photolithography fabrication of sol-gel ridge waveguides

    NASA Astrophysics Data System (ADS)

    Sara, Rahmani; Touam, Tahar; Blanchetiere, Chantal; Saddiki, Z.; Saravanamuttu, Kalaichelvi; Du, Xin M.; Chrostowski, Jacek; Andrews, Mark P.; Najafi, S. Iraj

    1998-07-01

    We report on fabrication of ridge waveguides in UV-light sensitive glass sol-gel thin films, deposited on silicon substrate, using a simple photolithography process. The single-layer films are prepared at low temperature and deep UV-light (DUV) is employed to make the waveguides. The effect of fabrication parameters on waveguide shape is investigated.

  12. A low-power, high-throughput maximum-likelihood convolutional decoder chip for NASA's 30/20 GHz program

    NASA Technical Reports Server (NTRS)

    Mccallister, R. D.; Crawford, J. J.

    1981-01-01

    It is pointed out that the NASA 30/20 GHz program will place in geosynchronous orbit a technically advanced communication satellite which can process time-division multiple access (TDMA) information bursts with a data throughput in excess of 4 GBPS. To guarantee acceptable data quality during periods of signal attenuation it will be necessary to provide a significant forward error correction (FEC) capability. Convolutional decoding (utilizing the maximum-likelihood techniques) was identified as the most attractive FEC strategy. Design trade-offs regarding a maximum-likelihood convolutional decoder (MCD) in a single-chip CMOS implementation are discussed.

  13. On the application of a fast polynomial transform and the Chinese remainder theorem to compute a two-dimensional convolution

    NASA Technical Reports Server (NTRS)

    Truong, T. K.; Lipes, R.; Reed, I. S.; Wu, C.

    1980-01-01

    A fast algorithm is developed to compute two dimensional convolutions of an array of d sub 1 X d sub 2 complex number points, where d sub 2 = 2(M) and d sub 1 = 2(m-r+) for some 1 or = r or = m. This algorithm requires fewer multiplications and about the same number of additions as the conventional fast fourier transform method for computing the two dimensional convolution. It also has the advantage that the operation of transposing the matrix of data can be avoided.

  14. Tally modifying of MCNP and post processing of pile-up simulation with time convolution method in PGNAA

    NASA Astrophysics Data System (ADS)

    Asghar Mowlavi, Ali; Koohi-Fayegh, Rahim

    2005-11-01

    Time convolution method has been employed for pile-up simulation in prompt gamma neutron activation analysis with an Am-Be neutron source and a 137Cs gamma source. A TALLYX subroutine has been written to design a new tally in the MCNP code. This tally records gamma particle information for the detector cell into an output file to be processed later. The times at which the particles are emitted by the source have been randomly generated following an exponential decay time distribution. A time convolution program was written to process the data produced and simulate more realistic pile-up. This method can be applied in optimization studies.

  15. Simulation of the shape from focus method using polychromatic point spread function

    NASA Astrophysics Data System (ADS)

    Hamarová, I.; Šmíd, P.; Horváth, P.

    2016-12-01

    Design of a model of a sensor based on the Shape from focus method is presented. The model uses polychromatic point spread functions of a generalized aperture function of lens and their convolution with an ideal image. The model approaches the reality and allows one to employ parameters of real components of the corresponding sensor, e.g. a spectrum of a light source, a dispersion function of a real imaging optical system and spectral sensitivity of a real light sensitive sensor. The model enables to study accuracy and reliability of the determination of the object's surface topography by means of the Shape from focus method.

  16. Ceramic for Silicon-Shaping Dies

    NASA Technical Reports Server (NTRS)

    Sekercioglu, I.; Wills, R. R.

    1982-01-01

    Silicon beryllium oxynitride (SiBON) is a promising candidate material for manufacture of shaping dies used in fabricating ribbons or sheets of silicon. It is extremely stable, resists thermal shock, and has excellent resistance to molten silicon. SiBON is a solid solution of beryllium silicate in beta-silicon nitride.

  17. Fabrication of Superhydrophilic Wool Fabrics By Nanotechnology

    NASA Astrophysics Data System (ADS)

    Chen, Dong

    Because of the fatty layer on its surface, wool fiber is hydrophobic, which results in poor water absorption and wicking properties that affect the comfort of wool textiles. The purpose of this research is to improve the wettability and comfort of wool textiles using nanotechnology. To reveal the knowledge gaps and ensure the originality of this study, a critical review of literature was conducted in relevant areas. To achieve the objectives of the research, a simple method for fabricating environmentally stable superhydrophilic wool fabrics was developed. Silica sols with diameters of 27 nm were prepared and then coated on the surface of pristine wool fibers to form an ultrathin layer, increasing both the surface roughness and energy. The morphology and composition of silica-sol-coated wool fabrics were characterized by a combination of SEM, TEM, FTIR, and XPS measurements. After evaluating the wettability and washing durability of the silica-sol-coated wool fabrics, it was found that the durability of these wool fabrics needed to be improved. To achieve superhydrophilic wool fabrics with good washing durability, reactive siloxane was functionalized on wool fiber surface, and an ultrathin silica nanoparticles layer was grafted on the surface by in-situ growth method. To evaluate the wettability change of silica grafted wool fabric, in addition to the contact angle, in-depth characterizations of water absorbing and drying properties of wool fabrics were measured. According to Chinese National Standard (GB/T 21655.1-2008 and GB/T 21655.2-2009), the prepared silica grafted wool fabric has excellent water absorbing and quick drying properties that can be maintained after washing 20 times in a washing machine. The strategy of siloxane bonding and in-situ growth was successfully extended to durable multifunctional wool fabrics combined with superhydrophilic, self-cleaning, and antibacterial properties. To study the relationships between functional properties and nano

  18. The effect of boundary shape to acoustic parameters

    NASA Astrophysics Data System (ADS)

    Prawirasasra, M. S.; Sampurna, R.; Suwandi

    2016-11-01

    To design a room in term of acoustic, many variables need to be considered such as volume, acoustic characteristics & surface area of material and also boundary shape. Modifying each variable possibly change the sound field character. To find impact of boundary shape, every needed properties is simulated through acoustic prediction software. The simulation is using three models with different geometry (asymmetry and symmetry) to produce certain objective parameters. By applying just noticeable difference (JND), the effect is considered known. Furthermore, individual perception is needed to gain subjective parameter. The test is using recorded speech that is convoluted with room impulse of each model. The result indicates that 84% of participants could not recognize the speech which is emit from different geometry properties. In contrast, JND value of T30 is exceed 5%. But for D50, every model has JND below 5%.

  19. Real-time dose computation: GPU-accelerated source modeling and superposition/convolution

    SciTech Connect

    Jacques, Robert; Wong, John; Taylor, Russell; McNutt, Todd

    2011-01-15

    Purpose: To accelerate dose calculation to interactive rates using highly parallel graphics processing units (GPUs). Methods: The authors have extended their prior work in GPU-accelerated superposition/convolution with a modern dual-source model and have enhanced performance. The primary source algorithm supports both focused leaf ends and asymmetric rounded leaf ends. The extra-focal algorithm uses a discretized, isotropic area source and models multileaf collimator leaf height effects. The spectral and attenuation effects of static beam modifiers were integrated into each source's spectral function. The authors introduce the concepts of arc superposition and delta superposition. Arc superposition utilizes separate angular sampling for the total energy released per unit mass (TERMA) and superposition computations to increase accuracy and performance. Delta superposition allows single beamlet changes to be computed efficiently. The authors extended their concept of multi-resolution superposition to include kernel tilting. Multi-resolution superposition approximates solid angle ray-tracing, improving performance and scalability with a minor loss in accuracy. Superposition/convolution was implemented using the inverse cumulative-cumulative kernel and exact radiological path ray-tracing. The accuracy analyses were performed using multiple kernel ray samplings, both with and without kernel tilting and multi-resolution superposition. Results: Source model performance was <9 ms (data dependent) for a high resolution (400{sup 2}) field using an NVIDIA (Santa Clara, CA) GeForce GTX 280. Computation of the physically correct multispectral TERMA attenuation was improved by a material centric approach, which increased performance by over 80%. Superposition performance was improved by {approx}24% to 0.058 and 0.94 s for 64{sup 3} and 128{sup 3} water phantoms; a speed-up of 101-144x over the highly optimized Pinnacle{sup 3} (Philips, Madison, WI) implementation. Pinnacle{sup 3

  20. Shape measurement biases from underfitting and ellipticity gradients

    DOE PAGES

    Bernstein, Gary M.

    2010-08-21

    With this study, precision weak gravitational lensing experiments require measurements of galaxy shapes accurate to <1 part in 1000. We investigate measurement biases, noted by Voigt and Bridle (2009) and Melchior et al. (2009), that are common to shape measurement methodologies that rely upon fitting elliptical-isophote galaxy models to observed data. The first bias arises when the true galaxy shapes do not match the models being fit. We show that this "underfitting bias" is due, at root, to these methods' attempts to use information at high spatial frequencies that has been destroyed by the convolution with the point-spread function (PSF)more » and/or by sampling. We propose a new shape-measurement technique that is explicitly confined to observable regions of k-space. A second bias arises for galaxies whose ellipticity varies with radius. For most shape-measurement methods, such galaxies are subject to "ellipticity gradient bias". We show how to reduce such biases by factors of 20–100 within the new shape-measurement method. The resulting shear estimator has multiplicative errors < 1 part in 103 for high-S/N images, even for highly asymmetric galaxies. Without any training or recalibration, the new method obtains Q = 3000 in the GREAT08 Challenge of blind shear reconstruction on low-noise galaxies, several times better than any previous method.« less

  1. Shape measurement biases from underfitting and ellipticity gradients

    SciTech Connect

    Bernstein, Gary M.

    2010-08-21

    With this study, precision weak gravitational lensing experiments require measurements of galaxy shapes accurate to <1 part in 1000. We investigate measurement biases, noted by Voigt and Bridle (2009) and Melchior et al. (2009), that are common to shape measurement methodologies that rely upon fitting elliptical-isophote galaxy models to observed data. The first bias arises when the true galaxy shapes do not match the models being fit. We show that this "underfitting bias" is due, at root, to these methods' attempts to use information at high spatial frequencies that has been destroyed by the convolution with the point-spread function (PSF) and/or by sampling. We propose a new shape-measurement technique that is explicitly confined to observable regions of k-space. A second bias arises for galaxies whose ellipticity varies with radius. For most shape-measurement methods, such galaxies are subject to "ellipticity gradient bias". We show how to reduce such biases by factors of 20–100 within the new shape-measurement method. The resulting shear estimator has multiplicative errors < 1 part in 103 for high-S/N images, even for highly asymmetric galaxies. Without any training or recalibration, the new method obtains Q = 3000 in the GREAT08 Challenge of blind shear reconstruction on low-noise galaxies, several times better than any previous method.

  2. ASIC-based architecture for the real-time computation of 2D convolution with large kernel size

    NASA Astrophysics Data System (ADS)

    Shao, Rui; Zhong, Sheng; Yan, Luxin

    2015-12-01

    Bidimensional convolution is a low-level processing algorithm of interest in many areas, but its high computational cost constrains the size of the kernels, especially in real-time embedded systems. This paper presents a hardware architecture for the ASIC-based implementation of 2-D convolution with medium-large kernels. Aiming to improve the efficiency of storage resources on-chip, reducing off-chip bandwidth of these two issues, proposed construction of a data cache reuse. Multi-block SPRAM to cross cached images and the on-chip ping-pong operation takes full advantage of the data convolution calculation reuse, design a new ASIC data scheduling scheme and overall architecture. Experimental results show that the structure can achieve 40× 32 size of template real-time convolution operations, and improve the utilization of on-chip memory bandwidth and on-chip memory resources, the experimental results show that the structure satisfies the conditions to maximize data throughput output , reducing the need for off-chip memory bandwidth.

  3. Deep Neural Networks as a Computational Model for Human Shape Sensitivity.

    PubMed

    Kubilius, Jonas; Bracci, Stefania; Op de Beeck, Hans P

    2016-04-01

    Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional 'deep' neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development.

  4. Deep Neural Networks as a Computational Model for Human Shape Sensitivity

    PubMed Central

    Op de Beeck, Hans P.

    2016-01-01

    Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional ‘deep’ neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development. PMID:27124699

  5. A high-order fast method for computing convolution integral with smooth kernel

    NASA Astrophysics Data System (ADS)

    Qiang, Ji

    2010-02-01

    In this paper we report on a high-order fast method to numerically calculate convolution integral with smooth non-periodic kernel. This method is based on the Newton-Cotes quadrature rule for the integral approximation and an FFT method for discrete summation. The method can have an arbitrarily high-order accuracy in principle depending on the number of points used in the integral approximation and a computational cost of O(Nlog(N)), where N is the number of grid points. For a three-point Simpson rule approximation, the method has an accuracy of O(h), where h is the size of the computational grid. Applications of the Simpson rule based algorithm to the calculation of a one-dimensional continuous Gauss transform and to the calculation of a two-dimensional electric field from a charged beam are also presented.

  6. Longitudinal Analysis of Discussion Topics in an Online Breast Cancer Community using Convolutional Neural Networks.

    PubMed

    Zhang, Shaodian; Grave, Edouard; Sklar, Elizabeth; Elhadad, Noémie

    2017-03-18

    Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out cross-sectional and longitudinal analyses to show topic distributions and topic dynamics throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification and identify several patterns and trajectories. For example, although members discuss mainly disease-related topics, their interest may change through time and vary with their disease severities.

  7. FAST-PT: a novel algorithm to calculate convolution integrals in cosmological perturbation theory

    NASA Astrophysics Data System (ADS)

    McEwen, Joseph E.; Fang, Xiao; Hirata, Christopher M.; Blazek, Jonathan A.

    2016-09-01

    We present a novel algorithm, FAST-PT, for performing convolution or mode-coupling integrals that appear in nonlinear cosmological perturbation theory. The algorithm uses several properties of gravitational structure formation—the locality of the dark matter equations and the scale invariance of the problem—as well as Fast Fourier Transforms to describe the input power spectrum as a superposition of power laws. This yields extremely fast performance, enabling mode-coupling integral computations fast enough to embed in Monte Carlo Markov Chain parameter estimation. We describe the algorithm and demonstrate its application to calculating nonlinear corrections to the matter power spectrum, including one-loop standard perturbation theory and the renormalization group approach. We also describe our public code (in Python) to implement this algorithm. The code, along with a user manual and example implementations, is available at https://github.com/JoeMcEwen/FAST-PT.

  8. Single-shot photonic time-intensity integration based on a time-spectrum convolution system.

    PubMed

    Malacarne, Antonio; Ashrafi, Reza; Li, Ming; LaRochelle, Sophie; Yao, Jianping; Azaña, José

    2012-04-15

    Real-time and single-shot ultra-fast photonic time-intensity integration of arbitrary temporal waveforms is proposed and demonstrated. The intensity-integration concept is based on a time-spectrum convolution system, where the use of a multi-wavelength laser with a flat envelope, employed as the incoherent broadband source, enables single-shot operation. The experimental implementation is based on optical intensity modulation of the multi-wavelength laser with the input waveform, followed by linear dispersion. In particular, photonic temporal intensity integration with a processing bandwidth of 36.8 GHz over an integration time window of 1.24 ns is verified by experimentally measuring the integration of an ultra-short microwave pulse and an arbitrary microwave waveform.

  9. Object class segmentation of RGB-D video using recurrent convolutional neural networks.

    PubMed

    Pavel, Mircea Serban; Schulz, Hannes; Behnke, Sven

    2017-04-01

    Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results.

  10. Automated detection of geological landforms on Mars using Convolutional Neural Networks

    NASA Astrophysics Data System (ADS)

    Palafox, Leon F.; Hamilton, Christopher W.; Scheidt, Stephen P.; Alvarez, Alexander M.

    2017-04-01

    The large volume of high-resolution images acquired by the Mars Reconnaissance Orbiter has opened a new frontier for developing automated approaches to detecting landforms on the surface of Mars. However, most landform classifiers focus on crater detection, which represents only one of many geological landforms of scientific interest. In this work, we use Convolutional Neural Networks (ConvNets) to detect both volcanic rootless cones and transverse aeolian ridges. Our system, named MarsNet, consists of five networks, each of which is trained to detect landforms of different sizes. We compare our detection algorithm with a widely used method for image recognition, Support Vector Machines (SVMs) using Histogram of Oriented Gradients (HOG) features. We show that ConvNets can detect a wide range of landforms and has better accuracy and recall in testing data than traditional classifiers based on SVMs.

  11. A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

    PubMed Central

    Quan, Chanqin

    2016-01-01

    The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method (1) only takes the sdp and word embedding as input and (2) could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task. PMID:27493967

  12. Resting State EEG-based biometrics for individual identification using convolutional neural networks.

    PubMed

    Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

    2015-08-01

    Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

  13. Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks.

    PubMed

    Yao, Kun; Parkhill, John

    2016-03-08

    We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from an input electron density. The output of the network is used as a nonlocal correction to conventional local and semilocal kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. The density which minimizes the total energy given by the functional is examined in detail. We identify several avenues to improve on this exploratory work, by reducing numerical noise and changing the structure of our functional. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.

  14. A high-order fast method for computing convolution integral with smooth kernel

    SciTech Connect

    Qiang, Ji

    2009-09-28

    In this paper we report on a high-order fast method to numerically calculate convolution integral with smooth non-periodic kernel. This method is based on the Newton-Cotes quadrature rule for the integral approximation and an FFT method for discrete summation. The method can have an arbitrarily high-order accuracy in principle depending on the number of points used in the integral approximation and a computational cost of O(Nlog(N)), where N is the number of grid points. For a three-point Simpson rule approximation, the method has an accuracy of O(h{sup 4}), where h is the size of the computational grid. Applications of the Simpson rule based algorithm to the calculation of a one-dimensional continuous Gauss transform and to the calculation of a two-dimensional electric field from a charged beam are also presented.

  15. Aquifer response to stream-stage and recharge variations. II. Convolution method and applications

    USGS Publications Warehouse

    Barlow, P.M.; DeSimone, L.A.; Moench, A.F.

    2000-01-01

    In this second of two papers, analytical step-response functions, developed in the companion paper for several cases of transient hydraulic interaction between a fully penetrating stream and a confined, leaky, or water-table aquifer, are used in the convolution integral to calculate aquifer heads, streambank seepage rates, and bank storage that occur in response to streamstage fluctuations and basinwide recharge or evapotranspiration. Two computer programs developed on the basis of these step-response functions and the convolution integral are applied to the analysis of hydraulic interaction of two alluvial stream-aquifer systems in the northeastern and central United States. These applications demonstrate the utility of the analytical functions and computer programs for estimating aquifer and streambank hydraulic properties, recharge rates, streambank seepage rates, and bank storage. Analysis of the water-table aquifer adjacent to the Blackstone River in Massachusetts suggests that the very shallow depth of water table and associated thin unsaturated zone at the site cause the aquifer to behave like a confined aquifer (negligible specific yield). This finding is consistent with previous studies that have shown that the effective specific yield of an unconfined aquifer approaches zero when the capillary fringe, where sediment pores are saturated by tension, extends to land surface. Under this condition, the aquifer's response is determined by elastic storage only. Estimates of horizontal and vertical hydraulic conductivity, specific yield, specific storage, and recharge for a water-table aquifer adjacent to the Cedar River in eastern Iowa, determined by the use of analytical methods, are in close agreement with those estimated by use of a more complex, multilayer numerical model of the aquifer. Streambank leakance of the semipervious streambank materials also was estimated for the site. The streambank-leakance parameter may be considered to be a general (or lumped

  16. Surface height map estimation from a single image using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Zhou, Xiaowei; Zhong, Guoqiang; Qi, Lin; Dong, Junyu; Pham, Tuan D.; Mao, Jianzhou

    2017-02-01

    Surface height map estimation is an important task in high-resolution 3D reconstruction. This task differs from general scene depth estimation in the fact that surface height maps contain more high frequency information or fine details. Existing methods based on radar or other equipments can be used for large-scale scene depth recovery, but might fail in small-scale surface height map estimation. Although some methods are available for surface height reconstruction based on multiple images, e.g. photometric stereo, height map estimation directly from a single image is still a challenging issue. In this paper, we present a novel method based on convolutional neural networks (CNNs) for estimating the height map from a single image, without any equipments or extra prior knowledge of the image contents. Experimental results based on procedural and real texture datasets show the proposed algorithm is effective and reliable.

  17. First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

    SciTech Connect

    Edelen, A. L.; Biedron, S. G.; Milton, S. V.; Edelen, J. P.

    2016-12-16

    At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operation. Here, we show results of a first step toward implementing such a controller: our trained CNN can predict multiple simulated downstream beam parameters at the Fermilab Accelerator Science and Technology (FAST) facility's low energy beamline using simulated virtual cathode laser images, gun phases, and solenoid strengths.

  18. Strategies for Effectively Visualizing a 3D Flow Using Volume Line Integral Convolution

    NASA Technical Reports Server (NTRS)

    Interrante, Victoria; Grosch, Chester

    1997-01-01

    This paper discusses strategies for effectively portraying 3D flow using volume line integral convolution. Issues include defining an appropriate input texture, clarifying the distinct identities and relative depths of the advected texture elements, and selectively highlighting regions of interest in both the input and output volumes. Apart from offering insights into the greater potential of 3D LIC as a method for effectively representing flow in a volume, a principal contribution of this work is the suggestion of a technique for generating and rendering 3D visibility-impeding 'halos' that can help to intuitively indicate the presence of depth discontinuities between contiguous elements in a projection and thereby clarify the 3D spatial organization of elements in the flow. The proposed techniques are applied to the visualization of a hot, supersonic, laminar jet exiting into a colder, subsonic coflow.

  19. A convolutional code-based sequence analysis model and its application.

    PubMed

    Liu, Xiao; Geng, Xiaoli

    2013-04-16

    A new approach for encoding DNA sequences as input for DNA sequence analysis is proposed using the error correction coding theory of communication engineering. The encoder was designed as a convolutional code model whose generator matrix is designed based on the degeneracy of codons, with a codon treated in the model as an informational unit. The utility of the proposed model was demonstrated through the analysis of twelve prokaryote and nine eukaryote DNA sequences having different GC contents. Distinct differences in code distances were observed near the initiation and termination sites in the open reading frame, which provided a well-regulated characterization of the DNA sequences. Clearly distinguished period-3 features appeared in the coding regions, and the characteristic average code distances of the analyzed sequences were approximately proportional to their GC contents, particularly in the selected prokaryotic organisms, presenting the potential utility as an added taxonomic characteristic for use in studying the relationships of living organisms.

  20. Adaptive pedestrian detection using convolutional neural network with dynamically adjusted classifier

    NASA Astrophysics Data System (ADS)

    Tang, Song; Ye, Mao; Zhu, Ce; Liu, Yiguang

    2017-01-01

    How to transfer the trained detector into the target scenarios has been an important topic for a long time in the field of computer vision. Unfortunately, most of the existing transfer methods need to keep source samples or label target samples in the detection phase. Therefore, they are difficult to apply to real applications. For this problem, we propose a framework that consists of a controlled convolutional neural network (CCNN) and a modulating neural network (MNN). In a CCNN, the parameters of the last layer, i.e., the classifier, are dynamically adjusted by a MNN. For each target sample, the CCNN adaptively generates a proprietary classifier. Our contributions include (1) the first detector-based unsupervised transfer method that is very suitable for real applications and (2) a new scheme of a dynamically adjusting classifier in which a new object function is invented. Experimental results confirm that our method can achieve state-of-the-art results on two pedestrian datasets.

  1. Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement

    NASA Astrophysics Data System (ADS)

    Jia, Xiaodong; Zhao, Ming; Di, Yuan; Jin, Chao; Lee, Jay

    2017-01-01

    Minimum Entropy Deconvolution (MED) filter, which is a non-parametric approach for impulsive signature detection, has been widely studied recently. Although the merits of the MED filter are manifold, this method tends to over highlight the dominant peaks and its performance becomes less stable when strong noise exists. In order to better understand the behavior of the MED filter, this study first investigated the mathematical fundamentals of the MED filter and then explained the reason why the MED filter tends to over highlight the dominant peaks. In order to pursue finer solutions for weak impulsive signature enhancement, the Convolutional Sparse Filter (CSF) is originally proposed in this work and the derivation of the CSF is presented in details. The superiority of the proposed CSF over the MED filter is validated by both simulated data and experimental data. The results demonstrate that CSF is an effective method for impulsive signature enhancement that could be applied in rotating machines for incipient fault detection.

  2. Encoding physiological signals as images for affective state recognition using convolutional neural networks.

    PubMed

    Yu, Guangliang; Li, Xiang; Song, Dawei; Zhao, Xiaozhao; Zhang, Peng; Hou, Yuexian; Hu, Bin; Guangliang Yu; Xiang Li; Dawei Song; Xiaozhao Zhao; Peng Zhang; Yuexian Hou; Bin Hu; Zhao, Xiaozhao; Hou, Yuexian; Li, Xiang; Hu, Bin; Zhang, Peng; Song, Dawei; Yu, Guangliang

    2016-08-01

    Affective state recognition based on multiple modalities of physiological signals has been a hot research topic. Traditional methods require designing hand-crafted features based on domain knowledge, which is time-consuming and has not achieved a satisfactory performance. On the other hand, conducting classification on raw signals directly can also cause some problems, such as the interference of noise and the curse of dimensionality. To address these problems, we propose a novel approach that encodes different modalities of data as images and use convolutional neural networks (CNN) to perform the affective state recognition task. We validate our aproach on the DECAF dataset in comparison with two state-of-the-art methods, i.e., the Support Vector Machines (SVM) and Random Forest (RF). Experimental results show that our aproach outperforms the baselines by 5% to 9%.

  3. Shape matching under affine transformation using normalization and multi-scale area integral features

    NASA Astrophysics Data System (ADS)

    Cai, Huiying; Zhu, Feng; Hao, Yingming; Lu, Rongrong

    2016-10-01

    Shape Matching under Affine Transformation (SMAT) is an important issue in shape analysis. Most of the existing SMAT methods are sensitive to noise or complicated because they usually need to extract the edge points or compute the high order function of the shape. To solve these problems, a new SMAT method which combines the low order shape normalization and the multi-scale area integral features is proposed. First, the shapes with affine transformation are normalized into their orthogonal representations according to the moments and an equivalent resample. This procedure transforms the shape by several linear operations: translations, scaling, and rotation, following by a resample operation. Second, the Multi-Scale Area Integral Features (MSAIF) of the shapes which are invariant to the orthogonal transformation (rotation and reflection transformation) are extracted. The MSAIF is a signature achieved through concatenating the area integral feature at a range of scales from fine to coarse. The area integral feature is an integration of the feature values, which are computed by convoluting the shape with an isotropic kernel and taking the complement, over the shape domain following by the normalization using the area of the shape. Finally, the matching of different shapes is performed according to the dissimilarity which is measured with the optimal transport distance. The performance of the proposed method is tested on the car dataset and the multi-view curve dataset. Experimental results show that the proposed method is efficient and robust, and can be used in many shape analysis works.

  4. Flexible Metal-Fabric Radiators

    NASA Technical Reports Server (NTRS)

    Cross, Cynthia; Nguyen, Hai D.; Ruemmele, Warren; Andish, Kambiz K.; McCalley, Sean

    2005-01-01

    Flexible metal-fabric radiators have been considered as alternative means of dissipating excess heat from spacecraft and space suits. The radiators also may be useful in such special terrestrial applications as rejecting heat from space-suit-like protective suits worn in hot work environments. In addition to flexibility and consequent ease of deployment and installation on objects of varying sizes and shapes, the main advantages of these radiators over conventional rigid radiators are that they weigh less and occupy less volume for a given amount of cooling capacity. A radiator of this type includes conventional stainless-steel tubes carrying a coolant fluid. The main radiating component consists of a fabric of interwoven aluminum-foil strips bonded to the tubes by use of a proprietary process. The strip/tube bonds are strong and highly thermally conductive. Coolant is fed to and from the tubes via flexible stainless-steel manifolds designed to accommodate flexing of, and minimize bending forces on, the fabric. The manifolds are sized to minimize pressure drops and distribute the flow of coolant evenly to all the tubes. The tubes and manifolds are configured in two independent flow loops for operational flexibility and protective redundancy.

  5. Photochemical cutting of fabrics

    DOEpatents

    Piltch, Martin S.

    1994-01-01

    Apparatus for the cutting of garment patterns from one or more layers of fabric. A laser capable of producing laser light at an ultraviolet wavelength is utilized to shine light through a pattern, such as a holographic phase filter, and through a lens onto the one or more layers of fabric. The ultraviolet laser light causes rapid photochemical decomposition of the one or more layers of fabric, but only along the pattern. The balance of the fabric of the one or more layers of fabric is undamaged.

  6. FULLY CONVOLUTIONAL NETWORKS FOR MULTI-MODALITY ISOINTENSE INFANT BRAIN IMAGE SEGMENTATION.

    PubMed

    Nie, Dong; Wang, Li; Gao, Yaozong; Shen, Dinggang

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement.

  7. Reducing weight precision of convolutional neural networks towards large-scale on-chip image recognition

    NASA Astrophysics Data System (ADS)

    Ji, Zhengping; Ovsiannikov, Ilia; Wang, Yibing; Shi, Lilong; Zhang, Qiang

    2015-05-01

    In this paper, we develop a server-client quantization scheme to reduce bit resolution of deep learning architecture, i.e., Convolutional Neural Networks, for image recognition tasks. Low bit resolution is an important factor in bringing the deep learning neural network into hardware implementation, which directly determines the cost and power consumption. We aim to reduce the bit resolution of the network without sacrificing its performance. To this end, we design a new quantization algorithm called supervised iterative quantization to reduce the bit resolution of learned network weights. In the training stage, the supervised iterative quantization is conducted via two steps on server - apply k-means based adaptive quantization on learned network weights and retrain the network based on quantized weights. These two steps are alternated until the convergence criterion is met. In this testing stage, the network configuration and low-bit weights are loaded to the client hardware device to recognize coming input in real time, where optimized but expensive quantization becomes infeasible. Considering this, we adopt a uniform quantization for the inputs and internal network responses (called feature maps) to maintain low on-chip expenses. The Convolutional Neural Network with reduced weight and input/response precision is demonstrated in recognizing two types of images: one is hand-written digit images and the other is real-life images in office scenarios. Both results show that the new network is able to achieve the performance of the neural network with full bit resolution, even though in the new network the bit resolution of both weight and input are significantly reduced, e.g., from 64 bits to 4-5 bits.

  8. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

    PubMed

    Shin, Hoo-Chang; Roth, Holger R; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel; Summers, Ronald M

    2016-05-01

    Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

  9. Efficient training algorithms for a class of shunting inhibitory convolutional neural networks.

    PubMed

    Tivive, Fok Hing Chi; Bouzerdoum, Abdesselam

    2005-05-01

    This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the performance of the training algorithms. Sixteen training algorithms are implemented for the three different variants of the proposed CoNN architecture: binary-, Toeplitz- and fully connected architectures. All implemented algorithms can train the three network architectures successfully, but their convergence speed vary markedly. In particular, the combination of LS with the new hybrid method and LS with the LM method achieve the best convergence rates in terms of number of training epochs. In addition, the classification accuracies of all three architectures are assessed using ten-fold cross validation. The results show that the binary- and Toeplitz-connected architectures outperform slightly the fully connected architecture: the lowest error rates across all training algorithms are 1.95% for Toeplitz-connected, 2.10% for the binary-connected, and 2.20% for the fully connected network. In general, the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods, the three variants of LM algorithm, and the new hybrid/LS method perform consistently well, achieving error rates of less than 3% averaged across all three architectures.

  10. FULLY CONVOLUTIONAL NETWORKS FOR MULTI-MODALITY ISOINTENSE INFANT BRAIN IMAGE SEGMENTATION

    PubMed Central

    Nie, Dong; Wang, Li; Gao, Yaozong; Shen, Dinggang

    2016-01-01

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement. PMID:27668065

  11. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks

    PubMed Central

    Ypsilantis, Petros-Pavlos; Siddique, Musib; Sohn, Hyon-Mok; Davies, Andrew; Cook, Gary; Goh, Vicky; Montana, Giovanni

    2015-01-01

    Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient’s response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a “radiomics” approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models. PMID:26355298

  12. Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

    PubMed Central

    Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

    2015-01-01

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

  13. Deep convolutional networks for automated detection of posterior-element fractures on spine CT

    NASA Astrophysics Data System (ADS)

    Roth, Holger R.; Wang, Yinong; Yao, Jianhua; Lu, Le; Burns, Joseph E.; Summers, Ronald M.

    2016-03-01

    Injuries of the spine, and its posterior elements in particular, are a common occurrence in trauma patients, with potentially devastating consequences. Computer-aided detection (CADe) could assist in the detection and classification of spine fractures. Furthermore, CAD could help assess the stability and chronicity of fractures, as well as facilitate research into optimization of treatment paradigms. In this work, we apply deep convolutional networks (ConvNets) for the automated detection of posterior element fractures of the spine. First, the vertebra bodies of the spine with its posterior elements are segmented in spine CT using multi-atlas label fusion. Then, edge maps of the posterior elements are computed. These edge maps serve as candidate regions for predicting a set of probabilities for fractures along the image edges using ConvNets in a 2.5D fashion (three orthogonal patches in axial, coronal and sagittal planes). We explore three different methods for training the ConvNet using 2.5D patches along the edge maps of `positive', i.e. fractured posterior-elements and `negative', i.e. non-fractured elements. An experienced radiologist retrospectively marked the location of 55 displaced posterior-element fractures in 18 trauma patients. We randomly split the data into training and testing cases. In testing, we achieve an area-under-the-curve of 0.857. This corresponds to 71% or 81% sensitivities at 5 or 10 false-positives per patient, respectively. Analysis of our set of trauma patients demonstrates the feasibility of detecting posterior-element fractures in spine CT images using computer vision techniques such as deep convolutional networks.

  14. Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier

    SciTech Connect

    Rocco, Dominick Rosario

    2016-03-01

    The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of Δm2 and θ23 and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74×1020 POT, 14 kt equivalent) to provide new best- fit measurements of sin2(θ23) = 0.43 (with a statistically-degenerate compliment near 0.60) and |Δm2 | = 2.48 × 10-3 eV2.

  15. High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

    PubMed

    Rajkomar, Alvin; Lingam, Sneha; Taylor, Andrew G; Blum, Michael; Mongan, John

    2017-02-01

    The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation.

  16. Improving the full spectrum fitting method: accurate convolution with Gauss-Hermite functions

    NASA Astrophysics Data System (ADS)

    Cappellari, Michele

    2017-04-01

    I start by providing an updated summary of the penalized pixel-fitting (PPXF) method that is used to extract the stellar and gas kinematics, as well as the stellar population of galaxies, via full spectrum fitting. I then focus on the problem of extracting the kinematics when the velocity dispersion σ is smaller than the velocity sampling ΔV that is generally, by design, close to the instrumental dispersion σinst. The standard approach consists of convolving templates with a discretized kernel, while fitting for its parameters. This is obviously very inaccurate when σ ≲ ΔV/2, due to undersampling. Oversampling can prevent this, but it has drawbacks. Here I present a more accurate and efficient alternative. It avoids the evaluation of the undersampled kernel and instead directly computes its well-sampled analytic Fourier transform, for use with the convolution theorem. A simple analytic transform exists when the kernel is described by the popular Gauss-Hermite parametrization (which includes the Gaussian as special case) for the line-of-sight velocity distribution. I describe how this idea was implemented in a significant upgrade to the publicly available PPXF software. The key advantage of the new approach is that it provides accurate velocities regardless of σ. This is important e.g. for spectroscopic surveys targeting galaxies with σ ≪ σinst, for galaxy redshift determinations or for measuring line-of-sight velocities of individual stars. The proposed method could also be used to fix Gaussian convolution algorithms used in today's popular software packages.

  17. Free-flow reabsorption of glucose, sodium, osmoles and water in rat proximal convoluted tubule.

    PubMed Central

    Bishop, J H; Green, R; Thomas, S

    1979-01-01

    1. Reabsorption of glucose, sodium, total solute (osmoles) and water in the rat proximal tubule (pars convoluta) were studied by free-flow micropuncture at normal (saline-infused), suppressed (saline with phlorizin) and elevated (glucose infusion) glucose reabsorption rates. 2. Phlorizin completely inhibited net glucose reabsorption, approximately halved reabsorption of sodium, total solutes and water, and reduced single nephron glomerular filtration rate (SNGFR). 3. In saline and glucose-infused groups, there were no significant differences between SNGFR nor between reabsorptions (fractional and absolute) of either sodium, total solute or water, which were uniformly distributed along segments assessible to micropuncture. 4. Glucose reabsorptive capacity existed along the entire pars convoluta, with highest reabsorptive rates in convolutions closest to the glomerulus (in saline-infused rats, 90% fractional reabsorption at 2 mm, over 95% at end pars convoluta; in glucose-infused rats, 55 and 90%, respectively). 5. In saline and glucose infused rats, a significant correlation existed between net glucose and sodium reabsorption, but the regression slopes differed and correlations became non-significant when the reabsorptive fluxes were factored by SNGFR. 6. For all groups, the majority of tubular fluid (TF) concentrations of osmoles and sodium were lower than those in plasma (over-all mean TFosm)Posm = 0.973 +/- 0.004, P less than 0.001; TFNa /PNa = 0.964 +/- 0.005, P less than 0.001). 7. Correspondingly, calculated osmolal and sodium concentrations in the reabsorbate were greater than those in plasma, and were significantly correlated with distance to puncture site with maximal values in the most proximal convolutions (for osmolality, approximately +79 m-osmole kg-1 water at 1 mm). PMID:469722

  18. Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

    PubMed

    Ypsilantis, Petros-Pavlos; Siddique, Musib; Sohn, Hyon-Mok; Davies, Andrew; Cook, Gary; Goh, Vicky; Montana, Giovanni

    2015-01-01

    Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.

  19. Comparison of fast Fourier transform and convolution in wavelength scanning interferometry

    NASA Astrophysics Data System (ADS)

    Muhamedsalih, H.; Jiang, X.; Gao, F.

    2011-05-01

    The assessment of surface finish has become increasingly important in the field of precision engineering. Optical interferometry has been widely used for surface measurement due to the advantages of non-contact and high accuracy interrogation. In spite of the 2π phase ambiguity that can limit the measurement scale in monochromatic interferometry, other optical interferomtry have succeeded to overcome this problem and to measure both rough and smooth surfaces such as white light interferometry and wavelength scanning interferometry (WSI). The WSI can be used to measure large discontinuous surface profiles by producing phase shifts without any mechanical scanning process. Where the WSI produces the phase shifts by altering the wavelength of a broadband light source and capturing the produced interferograms by a CCD. This paper introduces an optical setup and operation principle of a WSI that used a halogen white light as a broadband illumination source and an acousto-optic tunable filter (AOTF) as a wavelength scanning device. This setup can provide a wide scan range in the visible region. The scanned range is being operated from 682.8 nm to 552.8nm and the number of captured frames is 128. Furthermore, the obtained interferograms from a Linnik interferometer have been analyzed by two methods, Fast Fourier Transform and Convolution. A mathematical description of both methods is presented then a comparison in results accuracy is made between them. The Areal measurement of a standard 4.707μm step height sample shows that FFT and convolution methods could provide a nanometer measurement resolution for the surface finish inspection.

  20. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.

    PubMed

    Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

    2015-03-01

    The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement.

  1. Classification of teeth in cone-beam CT using deep convolutional neural network.

    PubMed

    Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

    2017-01-01

    Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic filing of dental charts for forensic identification.

  2. SU-E-T-371: Evaluating the Convolution Algorithm of a Commercially Available Radiosurgery Irradiator Using a Novel Phantom

    SciTech Connect

    Cates, J; Drzymala, R

    2015-06-15

    Purpose: The purpose of this study was to develop and use a novel phantom to evaluate the accuracy and usefulness of the Leskell Gamma Plan convolution-based dose calculation algorithm compared with the current TMR10 algorithm. Methods: A novel phantom was designed to fit the Leskell Gamma Knife G Frame which could accommodate various materials in the form of one inch diameter, cylindrical plugs. The plugs were split axially to allow EBT2 film placement. Film measurements were made during two experiments. The first utilized plans generated on a homogeneous acrylic phantom setup using the TMR10 algorithm, with various materials inserted into the phantom during film irradiation to assess the effect on delivered dose due to unplanned heterogeneities upstream in the beam path. The second experiment utilized plans made on CT scans of different heterogeneous setups, with one plan using the TMR10 dose calculation algorithm and the second using the convolution-based algorithm. Materials used to introduce heterogeneities included air, LDPE, polystyrene, Delrin, Teflon, and aluminum. Results: The data shows that, as would be expected, having heterogeneities in the beam path does induce dose delivery error when using the TMR10 algorithm, with the largest errors being due to the heterogeneities with electron densities most different from that of water, i.e. air, Teflon, and aluminum. Additionally, the Convolution algorithm did account for the heterogeneous material and provided a more accurate predicted dose, in extreme cases up to a 7–12% improvement over the TMR10 algorithm. The convolution algorithm expected dose was accurate to within 3% in all cases. Conclusion: This study proves that the convolution algorithm is an improvement over the TMR10 algorithm when heterogeneities are present. More work is needed to determine what the heterogeneity size/volume limits are where this improvement exists, and in what clinical and/or research cases this would be relevant.

  3. Method for Fabricating Composite Structures Using Continuous Press Forming

    NASA Technical Reports Server (NTRS)

    Farley, Gary L. (Inventor)

    1997-01-01

    A method for fabricating composite structures at a low-cost. moderate-to-high production rate. A first embodiment of the method includes employing a continuous press forming fabrication process. A second embodiment of the method includes employing a pultrusion process for obtaining composite structures. The methods include coating yarns with matrix material, weaving the yarn into fabric to produce a continuous fabric supply and feeding multiple layers of net-shaped fabrics having optimally oriented fibers into a debulking tool to form an undebulked preform. The continuous press forming fabrication process includes partially debulking the preform, cutting the partially debulked preform and debulking the partially debulked preform to form a net-shape. An electron-beam or similar technique then cures the structure. The pultrusion fabric process includes feeding the undebulked preform into a heated die and gradually debulking the undebulked preform. The undebulked preform in the heated die changes dimension until a desired cross-sectional dimension is achieved. This process further includes obtaining a net-shaped infiltrated uncured preform, cutting the uncured preform to a desired length and electron-beam curing (or similar technique) the uncured preform. These fabrication methods produce superior structures formed at higher production rates. resulting in lower cost and high structural performance.

  4. Method for Fabricating Composite Structures Using Pultrusion Processing

    NASA Technical Reports Server (NTRS)

    Farley, Gary L. (Inventor)

    2000-01-01

    A method for fabricating composite structures at a low-cost, moderate-to-high production rate. A first embodiment of the method includes employing a continuous press forming fabrication process. A second embodiment of the method includes employing a pultrusion process for obtaining composite structures. The methods include coating yarns with matrix material, weaving the yarn into fabric to produce a continuous fabric supply and feeding multiple layers of net-shaped fabrics having optimally oriented fibers into a debulking tool to form an undebulked preform. The continuous press forming fabrication process includes partially debulking the preform, cutting the partially debulked preform and debulking the partially debulked preform to form a net-shape. An electron-beam or similar technique then cures the structure. The pultrusion fabric process includes feeding the undebulked preform into a heated die and gradually debulking the undebulked preform. The undebulked preform in the heated die changes dimension until a desired cross-sectional dimension is achieved. This process further includes obtaining a net-shaped infiltrated uncured preform, cutting the uncured preform to a desired length and electron-beam curing (or similar technique) the uncured preform. These fabrication methods produce superior structures formed at higher production rates, resulting in lower cost and high structural performance.

  5. Shape-Shifting Plastic

    SciTech Connect

    2015-05-20

    A new plastic developed by ORNL and Washington State University transforms from its original shape through a series of temporary shapes and returns to its initial form. The shape-shifting process is controlled through changes in temperature

  6. Facile fabrication of silver nanofin array via electroless plating.

    PubMed

    Miyoshi, Kentaro; Aoki, Yoshitaka; Kunitake, Toyoki; Fujikawa, Shigenori

    2008-04-15

    The fabrication of metallic nanostructures is one of the main issues in nanotechnology. This article describes the fabrication of a silver nanofin array by combining microlithography, electroless plating, and an etching technique. Fabricated Ag nanofins have a high aspect ratio (height/width = 10, width = 60 nm, height = 600 nm), and their widths and heights can be controlled by the period of electroless plating and the height of the original line pattern. An isolated Ag nanofin was proven to show metallic electrical conductivity. The current process provides a rapid and shape-designable fabrication method of metallic nanostructures.

  7. Tensile test of dumbbell-shaped specimen in thickness direction

    NASA Astrophysics Data System (ADS)

    Iizuka, Takashi

    2016-10-01

    Sheet metal forming is widely used in manufacturing shops, and evaluation of forming limit for sheet metal is important. However, specimen shape influences on the fracture of the sheet metal. As one of methods to decrease these effects, an uniaxial tensile test using specimen dumbbell-shaped in thickness direction had been examined using FEM analysis. In this study, actually specimen dumbbell-shaped in thickness direction was fabricated using a new incremental sheet forging method, and uniaxial tensile test was conducted. Load-stroke diagram, fracture morphologies, stress-strain curves and shape after fracture were investigated, and effects of specimen shape were considered. Elongation was larger as using specimen dumbbell-shaped in the width direction. Stress-strain curves until necking occurred were less influenced by specimen shape. However, yield stress decreased and local elongation increased as using specimen dumbbell-shaped in the width direction. The reasons why these tendencies showed were considered in the view of specimen shapes.

  8. Energy-beam-driven rapid fabrication system

    DOEpatents

    Keicher, David M.; Atwood, Clinton L.; Greene, Donald L.; Griffith, Michelle L.; Harwell, Lane D.; Jeantette, Francisco P.; Romero, Joseph A.; Schanwald, Lee P.; Schmale, David T.

    2002-01-01

    An energy beam driven rapid fabrication system, in which an energy beam strikes a growth surface to form a molten puddle thereon. Feed powder is then injected into the molten puddle from a converging flow of feed powder. A portion of the feed powder becomes incorporated into the molten puddle, forcing some of the puddle contents to freeze on the growth surface, thereby adding an additional layer of material. By scanning the energy beam and the converging flow of feed powder across the growth surface, complex three-dimensional shapes can be formed, ready or nearly ready for use. Nearly any class of material can be fabricated using this system.

  9. Magnetic fabric, welding texture and strain fabric in the Nuraxi Tuff, Sardinia, Italy

    NASA Astrophysics Data System (ADS)

    Pioli, L.; Lanza, R.; Ort, M.; Rosi, M.

    2008-09-01

    Anisotropy of magnetic susceptibility (AMS) has been used to interpret flow directions in ignimbrites, but no study has demonstrated that the AMS fabric corresponds to the flow fabric. In this paper, we show that the AMS and strain fabric coincide in a high-grade ignimbrite, the Nuraxi Tuff, a Miocene rhyolitic ignimbrite displaying a wide variability of rheomorphic features and a well-defined magnetic fabric. Natural remanent magnetization (NRM) data indicate that the magnetization of the tuff is homogeneous and was acquired at high temperatures by Ti-magnetite crystals. Comparison between the magnetic fabric and the deformation features along a representative section shows that AMS and anisotropy of isothermal remanent magnetization (AIRM) fabric are coaxial with and reproduce the shape of the strain ellipsoid. Magnetic tests and scanning electron microscopy observations indicate that the fabric is due to trails of micrometer-size, pseudo-single domain, magnetically interacting magnetite crystals. Microlites formed along discontinuities such as shard rims and vesicle walls mimicking the petrofabric of the tuff. The fabric was thus acquired after deposition, before late rheomorphic processes, and accurately mimics homogeneous deformation features of the shards during welding processes and mass flow.

  10. Ultrasonic-aided fabrication of gold nanofluids.

    PubMed

    Chen, Hui-Jiuan; Wen, Dongsheng

    2011-03-07

    A novel ultrasonic-aided one-step method for the fabrication of gold nanofluids is proposed in this study. Both spherical- and plate-shaped gold nanoparticles (GNPs) in the size range of 10-300 nm are synthesized. Subsequent purification produces well-controlled nanofluids with known solid and liquid contents. The morphology and properties of the nanoparticle and nanofluids are characterized by transmission electron microscopy, scanning electron microscope, energy dispersive X-ray spectroscope, X-ray diffraction spectroscopy, and dynamic light scattering, as well as effective thermal conductivities. The ultrasonication technique is found to be a very powerful tool in engineering the size and shape of GNPs. Subsequent property measurement shows that both particle size and particle shape play significant roles in determining the effective thermal conductivity. A large increase in effective thermal conductivity can be achieved (approximately 65%) for gold nanofluids using plate-shaped particles under low particle concentrations (i.e.764 μM/L).

  11. Tactile Fabric Panel in an Eight Zones Structure

    PubMed Central

    Alsina, Maria; Escudero, Francesc; Margalef, Jordi; Luengo, Sonia

    2007-01-01

    By introducing a percentage of conductive material during the manufacture of sewing thread, it is possible to obtain a fabric which is able to detect variations in pressure in certain areas. In previous experiments the existence of resistance variations has been demonstrated, although some constrains of cause and effect were found in the fabric. The research has been concentrated in obtaining a fabric that allows electronic detection of its shape changes. Additionally, and because a causal behavior is needed, it is necessary that the fabric recovers its original shape when the external forces cease. The structure of the fabric varies with the type of deformation applied. Two kinds of deformation are described: those caused by stretching and those caused by pressure. This last type of deformation gives different responses depending on the conductivity of the object used to cause the pressure. This effect is related to the type of thread used to manufacture the fabric. So, if the pressure is caused by a finger the response is different compared to the response caused by a conductive object. Another fact that has to be mentioned is that a pressure in a specific point of the fabric can affect other detection points depending on the force applied. This effect is related to the fabric structure. The goals of this article are validating the structure of the fabric used, as well as the study of the two types of deformation mentioned before.

  12. Mechanical design of a shape memory alloy actuated prosthetic hand.

    PubMed

    De Laurentis, Kathryn J; Mavroidis, Constantinos

    2002-01-01

    This paper presents the mechanical design for a new five fingered, twenty degree-of-freedom dexterous hand patterned after human anatomy and actuated by Shape Memory Alloy artificial muscles. Two experimental prototypes of a finger, one fabricated by traditional means and another fabricated by rapid prototyping techniques, are described and used to evaluate the design. An important aspect of the Rapid Prototype technique used here is that this multi-articulated hand will be fabricated in one step, without requiring assembly, while maintaining its desired mobility. The use of Shape Memory Alloy actuators combined with the rapid fabrication of the non-assembly type hand, reduce considerably its weight and fabrication time. Therefore, the focus of this paper is the mechanical design of a dexterous hand that combines Rapid Prototype techniques and smart actuators. The type of robotic hand described in this paper can be utilized for applications requiring low weight, compactness, and dexterity such as prosthetic devices, space and planetary exploration.

  13. T-Shaped Emitter Metal Structures for HBTs

    NASA Technical Reports Server (NTRS)

    Fung, King Man; Samoska, Lorene; Velebir, James; Muller, Richard; Echternach, Pierre; Siegel, Peter; Smith, Peter; Martin, Suzanne; Malik, Roger; Rodwell, Mark; Urteaga, Miguel; Paidi, Vamsi; Griffith, Zack

    2006-01-01

    Metal emitter structures in a class of developmental InP-based high-speed heterojunction bipolar transistors (HBTs) have been redesigned to have T-shaped cross sections. T-cross-section metal features have been widely used in Schottky diodes and high-electron-mobility transistors, but not in HBTs. As explained, the purpose served by the present T cross-sectional shapes is to increase fabrication yields beyond those achievable with the prior cross-sectional shapes.

  14. Fabrics for aeronautic construction

    NASA Technical Reports Server (NTRS)

    Walen, E D

    1918-01-01

    The Bureau of Standards undertook the investigation of airplane fabrics with the view of finding suitable substitutes for the linen fabrics, and it was decided that the fibers to be considered were cotton, ramie, silk, and hemp. Of these, the cotton fiber was the logical one to be given primary consideration. Report presents the suitability, tensibility and stretching properties of cotton fabric obtained by laboratory tests.

  15. Crimp-Imbalanced Fabrics

    DTIC Science & Technology

    2011-03-30

    invention relates to crimped fabrics which are formed by using various textile architecture such as woven, braided, knitted or other known fabric in which...solution that substantially coats the yarn. The removable coating has a thickness that ensures a proper amount of crimp in the yarn. The tensions in...7 depicts a prior art non-woven cross-ply laminate ; [0037] FIG. 8 depicts a prior art example of balanced crimping in plain-woven fabric; 12

  16. Optical Fabrication Nightmares

    NASA Astrophysics Data System (ADS)

    Voras, Robert P.

    1980-09-01

    Optical fabrication nightmares come in a variety of forms. They are generally caused by "toos": too thin, too thick, too large, too small, too many, too few, etc. In practice I believe many optical fabrication problems could be eliminated - or at least minimized -if there were more communication between the designer and the process engineer, up front. However, since the purpose of this paper is to describe difficult items to fabricate and possible solutions for their fabrication, I will get off my soap-box and proceed to my assigned task.

  17. Quantifying the effect of rheology on plan-view shapes of lava flows

    NASA Technical Reports Server (NTRS)

    Bruno, B. C.; Taylor, G. J.; Lopes-Gautier, R. M. C.

    1993-01-01

    This study aims at quantifying the effect of rheology on the plan-view shapes of lava flows. Plan-view shapes of lava flows are important because they reflect the processes governing flow emplacement and may provide insight into lava flow rheology and dynamics. In our earlier investigation, it was reported that plan-view shapes of tholeite basalts are fractal, having a characteristic shape regardless of scale. It was also found one could use the fractal dimension (a parameter which quantifies flow margin convolution) to distinguish between the two major types of basalts: a'a and pahoehoe. Encouraged by these earlier results, a similar method for use on silicic flows are being developed and our preliminary work is presented.

  18. Direct Release of Sombrero-Shaped Magnetite Nanoparticles via Nanoimprint Lithography

    SciTech Connect

    Kwon, Byung Seok; Zhang, Wei; Li, Zheng; Krishnan, Kannan M.

    2015-01-10

    Magnetic nanoparticles produced via nanoimprint lithography can change the current paradigm of fabrication processes from chemical “bottom-up” synthesis to “top-down” fabrication. The combination of controlled nondirectional magnetron sputtering, ETFE mold, bilayer lift-off, and dry etching release can control the shape, size, and structure of the fabricated nanoparticles. The resulting magnetic nanoparticles have a novel “sombrero” shape with complex and unique physical/magnetic properties.

  19. From Shape to Letters.

    ERIC Educational Resources Information Center

    Schiller, Hillel A.

    In order to make letter shape recognition an integral part of perception training, the use of the line in its two basic shapes is proposed. Letter shapes may seem exceedingly complex linear shapes to young minds. Thus instead of instruction in configuration, instruction involving transformational activities to manipulate and create the…

  20. Seismic body wave separation in volcano-tectonic activity inferred by the Convolutive Independent Component Analysis

    NASA Astrophysics Data System (ADS)

    Capuano, Paolo; De Lauro, Enza; De Martino, Salvatore; Falanga, Mariarosaria; Petrosino, Simona

    2015-04-01

    One of the main challenge in volcano-seismological literature is to locate and characterize the source of volcano/tectonic seismic activity. This passes through the identification at least of the onset of the main phases, i.e. the body waves. Many efforts have been made to solve the problem of a clear separation of P and S phases both from a theoretical point of view and developing numerical algorithms suitable for specific cases (see, e.g., Küperkoch et al., 2012). Recently, a robust automatic procedure has been implemented for extracting the prominent seismic waveforms from continuously recorded signals and thus allowing for picking the main phases. The intuitive notion of maximum non-gaussianity is achieved adopting techniques which involve higher-order statistics in frequency domain., i.e, the Convolutive Independent Component Analysis (CICA). This technique is successful in the case of the blind source separation of convolutive mixtures. In seismological framework, indeed, seismic signals are thought as the convolution of a source function with path, site and the instrument response. In addition, time-delayed versions of the same source exist, due to multipath propagation typically caused by reverberations from some obstacle. In this work, we focus on the Volcano Tectonic (VT) activity at Campi Flegrei Caldera (Italy) during the 2006 ground uplift (Ciaramella et al., 2011). The activity was characterized approximately by 300 low-magnitude VT earthquakes (Md < 2; for the definition of duration magnitude, see Petrosino et al. 2008). Most of them were concentrated in distinct seismic sequences with hypocenters mainly clustered beneath the Solfatara-Accademia area, at depths ranging between 1 and 4 km b.s.l.. The obtained results show the clear separation of P and S phases: the technique not only allows the identification of the S-P time delay giving the timing of both phases but also provides the independent waveforms of the P and S phases. This is an enormous